%%% -*-BibTeX-*-
%%% ====================================================================
%%%  BibTeX-file{
%%%     author          = "Nelson H. F. Beebe",
%%%     version         = "1.04",
%%%     date            = "28 March 2021",
%%%     time            = "07:30:00 MDT",
%%%     filename        = "tompecs.bib",
%%%     address         = "University of Utah
%%%                        Department of Mathematics, 110 LCB
%%%                        155 S 1400 E RM 233
%%%                        Salt Lake City, UT 84112-0090
%%%                        USA",
%%%     telephone       = "+1 801 581 5254",
%%%     FAX             = "+1 801 581 4148",
%%%     URL             = "http://www.math.utah.edu/~beebe",
%%%     checksum        = "53394 4987 27856 261981",
%%%     email           = "beebe at math.utah.edu, beebe at acm.org,
%%%                        beebe at computer.org (Internet)",
%%%     codetable       = "ISO/ASCII",
%%%     keywords        = "ACM Transactions on Modeling and Performance
%%%                        Evaluation of Computing Systems (TOMPECS);
%%%                        bibliography; BibTeX",
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%%%     docstring       = "This is a COMPLETE BibTeX bibliography for
%%%                        ACM Transactions on Modeling and Performance
%%%                        Evaluation of Computing Systems (TOMPECS)
%%%                        (CODEN ????, ISSN 2376-3639 (print),
%%%                        2376-3647 (electronic)).  The journal appears
%%%                        quarterly, and publication began with volume
%%%                        1, number 1, in March 2016.
%%%
%%%                        At version 1.04, the COMPLETE journal
%%%                        coverage looked like this:
%%%
%%%                             2016 (  26)    2018 (  22)    2020 (  14)
%%%                             2017 (  17)    2019 (  23)    2021 (   4)
%%%
%%%                             Article:        106
%%%
%%%                             Total entries:  106
%%%
%%%                        The journal Web page can be found at:
%%%
%%%                            http://tompecs.acm.org/
%%%
%%%
%%%                            http://dl.acm.org/pub.cfm?id=J1525
%%%                            https://dl.acm.org/loi/tompecs
%%%
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%%%                        text of recent articles in PDF form.
%%%
%%%                        The initial draft was extracted from the ACM
%%%                        Web pages.
%%%
%%%                        ACM copyrights explicitly permit abstracting
%%%                        with credit, so article abstracts, keywords,
%%%                        and subject classifications have been
%%%                        included in this bibliography wherever
%%%                        available.  Article reviews have been
%%%                        omitted, until their copyright status has
%%%                        been clarified.
%%%
%%%                        bibsource keys in the bibliography entries
%%%                        below indicate the entry originally came
%%%                        from the computer science bibliography
%%%                        archive, even though it has likely since
%%%                        been corrected and updated.
%%%
%%%                        URL keys in the bibliography point to
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%%%
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%%%
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%%%                        publication order, using bibsort -byvolume.''
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%%% ====================================================================
%%% Acknowledgement abbreviations:

@String{ack-nhfb = "Nelson H. F. Beebe,
University of Utah,
Department of Mathematics, 110 LCB,
155 S 1400 E RM 233,
Salt Lake City, UT 84112-0090, USA,
Tel: +1 801 581 5254,
FAX: +1 801 581 4148,
e-mail: \path|beebe@math.utah.edu|,
\path|beebe@acm.org|,
\path|beebe@computer.org| (Internet),
URL: \path|http://www.math.utah.edu/~beebe/|"}


%%% ====================================================================
%%% Journal abbreviations:

@String{j-TOMPECS               = "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)"}


%%% ====================================================================
%%% Bibliography entries:

@Article{Towsley:2016:I,
author =       "Don Towsley and Carey Williamson",
title =        "Introduction",
journal =      j-TOMPECS,
volume =       "1",
number =       "1",
pages =        "1:1--1:1",
month =        mar,
year =         "2016",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/2893179",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Jun 15 12:29:10 MDT 2017",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "http://dl.acm.org/citation.cfm?id=2893179",
acknowledgement = ack-nhfb,
articleno =    "1",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Lashgar:2016:ESM,
title =        "Employing Software-Managed Caches in {OpenACC}:
Opportunities and Benefits",
journal =      j-TOMPECS,
volume =       "1",
number =       "1",
pages =        "2:1--2:34",
month =        mar,
year =         "2016",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/2798724",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Jun 15 12:29:10 MDT 2017",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "http://dl.acm.org/citation.cfm?id=2798724",
abstract =     "The OpenACC programming model has been developed to
simplify accelerator programming and improve
investigate the main limitations faced by OpenACC in
harnessing all capabilities of GPU-like accelerators.
We build on our findings and discuss the opportunity to
exploit a software-managed cache as (i) a fast
communication medium and (ii) a cache for data reuse.
To this end, we propose a new directive and
communication model for OpenACC. Investigating several
benchmarks, we show that the proposed directive can
improve performance up to $2.54 \times$, and at the
cost of minor programming effort.",
acknowledgement = ack-nhfb,
articleno =    "2",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Zhang:2016:VSL,
author =       "Deli Zhang and Jeremiah Wilke and Gilbert Hendry and
Damian Dechev",
title =        "Validating the Simulation of Large-Scale Parallel
Applications Using Statistical Characteristics",
journal =      j-TOMPECS,
volume =       "1",
number =       "1",
pages =        "3:1--3:22",
month =        mar,
year =         "2016",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/2809778",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Jun 15 12:29:10 MDT 2017",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "http://dl.acm.org/citation.cfm?id=2809778",
abstract =     "Simulation is a widely adopted method to analyze and
predict the performance of large-scale parallel
applications. Validating the hardware model is highly
important for complex simulations with a large number
of parameters. Common practice involves calculating the
percent error between the projected and the real
execution time of a benchmark program. However, in a
high-dimensional parameter space, this coarse-grained
approach often suffers from parameter insensitivity,
which may not be known a priori. Moreover, the
traditional approach cannot be applied to the
validation of software models, such as application
skeletons used in online simulations. In this work, we
present a methodology and a toolset for validating both
hardware and software models by quantitatively
comparing fine-grained statistical characteristics
obtained from execution traces. Although statistical
information has been used in tasks like performance
optimization, this is the first attempt to apply it to
simulation validation. Our experimental results show
that the proposed evaluation approach offers
significant improvement in fidelity when compared to
evaluation using total execution time, and the proposed
metrics serve as reliable criteria that progress toward
automating the simulation tuning process.",
acknowledgement = ack-nhfb,
articleno =    "3",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Roos:2016:DDE,
author =       "Stefanie Roos and Thorsten Strufe",
title =        "Dealing with Dead Ends: Efficient Routing in
Darknets",
journal =      j-TOMPECS,
volume =       "1",
number =       "1",
pages =        "4:1--4:30",
month =        mar,
year =         "2016",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/2809779",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Jun 15 12:29:10 MDT 2017",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "http://dl.acm.org/citation.cfm?id=2809779",
abstract =     "Darknets, membership-concealing peer-to-peer networks,
suffer from high message delivery delays due to
insufficient routing strategies. They form topologies
restricted to a subgraph of the social network of their
users by limiting connections to peers with a mutual
trust relationship in real life. Whereas centralized,
highly successful social networking services entail a
privacy loss of their users, Darknets at higher
performance represent an optimal private and
censorship-resistant communication substrate for social
applications. Decentralized routing so far has been
analyzed under the assumption that the network
resembles a perfect lattice structure. Freenet,
currently the only widely used Darknet, attempts to
approximate this structure by embedding the social
graph into a metric space. Considering the resulting
distortion, the common greedy routing algorithm is
adapted to account for local optima. Yet the impact of
thus suggest a model integrating inaccuracies in the
embedding. In the context of this model, we show that
the Freenet routing algorithm cannot achieve polylog
performance. Consequently, we design NextBestOnce, a
provable poylog algorithm based only on information
about neighbors. Furthermore, we show that the routing
length of NextBestOnce is further decreased by more
than a constant factor if neighbor-of-neighbor
information is included in the decision process.",
acknowledgement = ack-nhfb,
articleno =    "4",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Izagirre:2016:STA,
author =       "A. Izagirre and U. Ayesta and I. M. Verloop",
title =        "Sojourn Time Approximations for a Discriminatory
Processor Sharing Queue",
journal =      j-TOMPECS,
volume =       "1",
number =       "1",
pages =        "5:1--5:31",
month =        mar,
year =         "2016",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/2812807",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Jun 15 12:29:10 MDT 2017",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "http://dl.acm.org/citation.cfm?id=2812807",
abstract =     "We study a multiclass time-sharing discipline with
relative priorities known as discriminatory processor
sharing (DPS), which provides a natural framework to
model service differentiation in systems. The analysis
of DPS is extremely challenging, and analytical results
are scarce. We develop closed-form approximations for
the mean conditional (on the service requirement) and
unconditional sojourn times. The main benefits of the
approximations lie in its simplicity, the fact that it
applies for general service requirements with finite
second moments, and that it provides insights into the
dependency of the performance on the system parameters.
We show that the approximation for the mean conditional
and unconditional sojourn time of a customer is
decreasing as its relative priority increases. We also
show that the approximation is exact in various
scenarios, and that it is uniformly bounded in the
second moments of the service requirements. Finally, we
numerically illustrate that the approximation for
exponential, hyperexponential, and Pareto service
requirements is accurate across a broad range of
parameters.",
acknowledgement = ack-nhfb,
articleno =    "5",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Harrison:2016:EPT,
author =       "Peter G. Harrison and Naresh M. Patel and William J.
Knottenbelt",
title =        "Energy--Performance Trade-Offs via the {EP} Queue",
journal =      j-TOMPECS,
volume =       "1",
number =       "2",
pages =        "6:1--6:31",
month =        jun,
year =         "2016",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/2818726",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Jun 15 12:19:54 MDT 2017",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "http://dl.acm.org/citation.cfm?id=2818726",
abstract =     "We introduce the EP queue -- a significant
generalization of the M B / G /1 queue that has
state-dependent service time probability distributions
and incorporates power-up for first arrivals and
power-down for idle periods. We derive exact results
for the busy-time and response-time distributions. From
these, we derive power consumption metrics during
nonidle periods and overall response time metrics,
which together provide a single measure of the
trade-off between energy and performance. We illustrate
these trade-offs for some policies and show how
numerical results can provide insights into system
behavior. The EP queue has application to storage
systems, especially hard disks, and other data-center
components such as compute servers, networking, and
even hyperconverged infrastructure.",
acknowledgement = ack-nhfb,
articleno =    "6",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Tavakkol:2016:PED,
author =       "Arash Tavakkol and Pooyan Mehrvarzy and Mohammad
title =        "Performance Evaluation of Dynamic Page Allocation
Strategies in {SSDs}",
journal =      j-TOMPECS,
volume =       "1",
number =       "2",
pages =        "7:1--7:33",
month =        jun,
year =         "2016",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/2829974",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Jun 15 12:19:54 MDT 2017",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "http://dl.acm.org/citation.cfm?id=2829974",
abstract =     "Solid-state drives (SSDs) with tens of NAND flash
chips and highly parallel architectures are widely used
in enterprise and client storage systems. As any write
operation in NAND flash is preceded by a slow erase
operation, an out-of-place update mechanism is used to
distribute writes through SSD storage space to postpone
erase operations as far as possible. SSD controllers
use a mapping table along with a specific allocation
strategy to map logical host addresses to physical page
addresses within storage space. The allocation strategy
is further responsible for accelerating I/O operations
through better striping of physical addresses over SSD
parallel resources. Proposals already exist for using
static logical-to-physical address mapping that does
not balance the I/O traffic load within the SSD, and
its efficiency highly depends on access patterns. A
more balanced distribution of I/O operations is to
alternate resource allocation in a round-robin manner
irrespective of logical addresses. The number of
resources that can be dynamically allocated in this
fashion is defined as the degree of freedom, and to the
best of our knowledge, there has been no research thus
far to show what happens if different degrees of
explores the possibility of using dynamic resource
allocation and identifies key design opportunities that
it presents to improve SSD performance. Specifically,
using steady-state analysis of SSDs, we show that
dynamism helps to mitigate performance and endurance
experiments indicate that midrange/high-end SSDs with
dynamic allocation can provide I/O operations per
second (IOPS) improvement of up to 3.3x/9.6x, response
time improvement of up to 56\%/32\%, and about
88\%/96\% average reduction in the standard deviation
of erase counts of NAND flash blocks.",
acknowledgement = ack-nhfb,
articleno =    "7",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Chang:2016:CRA,
author =       "Cheng-Shang Chang and Jay Cheng and Tien-Ke Huang and
Duan-Shin Lee and Cheng-Yu Chen",
title =        "Coding Rate Analysis of Forbidden Overlap Codes in
High-Speed Buses",
journal =      j-TOMPECS,
volume =       "1",
number =       "2",
pages =        "8:1--8:25",
month =        jun,
year =         "2016",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/2846091",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Jun 15 12:19:54 MDT 2017",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "http://dl.acm.org/citation.cfm?id=2846091",
abstract =     "One of the main problems in deep submicron designs of
high-speed buses is propagation delay due to the
crosstalk effect. To alleviate the crosstalk effect,
there are several types of crosstalk avoidance codes
the coding rates of forbidden overlap codes (FOCs) that
avoid 010 $\to$ 101'' transition and 101 $\to$
010'' transition on any three adjacent wires in a bus.
We first compute the maximum achievable coding rate of
FOCs and the maximum coding rate of memoryless FOCs.
Our numerical results show that there is a significant
gap between the maximum coding rate of memoryless FOCs
and the maximum achievable rate. We then analyze the
coding rates of FOCs generated from the bit-stuffing
algorithm. Our worst-case analysis yields a tight lower
bound of the coding rate of the bit-stuffing algorithm.
Under the assumption of Bernoulli inputs, we use a
Markov chain model to compute the coding rate of a bus
with n wires under the bit-stuffing algorithm. The main
difficulty of solving such a Markov chain model is that
the number of states grows exponentially with respect
to the number of wires n. To tackle the problem of the
curse of dimensionality, we derive an approximate
analysis that leads to a recursive closed-form formula
for the coding rate over the n th wire. Our
approximations match extremely well with the numerical
results from solving the original Markov chain for $n \leq 10$ and the simulation results for $n \leq 3000$. Our analysis of coding rates of FOCs could be
propagation delay and coding rate among various
crosstalk avoidance codes in the literature. In
comparison with the forbidden transition codes (FTCs)
that have shorter propagation delay than that of FOCs,
our numerical results show that the coding rates of
FOCs are much higher than those of FTCs.",
acknowledgement = ack-nhfb,
articleno =    "8",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Bermolen:2016:ETP,
author =       "Paola Bermolen and Matthieu Jonckheere and Federico
Larroca and Pascal Moyal",
title =        "Estimating the Transmission Probability in Wireless
Networks with Configuration Models",
journal =      j-TOMPECS,
volume =       "1",
number =       "2",
pages =        "9:1--9:23",
month =        jun,
year =         "2016",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/2858795",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Jun 15 12:19:54 MDT 2017",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "http://dl.acm.org/citation.cfm?id=2858795",
abstract =     "We propose a new methodology to estimate the
probability of successful transmissions for random
access scheduling in wireless networks, in particular
those using Carrier Sense Multiple Access (CSMA).
Instead of focusing on spatial configurations of users,
we model the interference between users as a random
graph. Using configuration models for random graphs, we
show how the properties of the medium access mechanism
are captured by some deterministic differential
equations when the size of the graph gets large.
Performance indicators such as the probability of
connection of a given node can then be efficiently
computed from these equations. We also perform
simulations to illustrate the results on different
types of random graphs. Even on spatial structures,
these estimates get very accurate as soon as the
variance of the interference is not negligible.",
acknowledgement = ack-nhfb,
articleno =    "9",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Yan:2016:PAF,
author =       "Feng Yan and Xenia Mountrouidou and Alma Riska and
Evgenia Smirni",
title =        "{PREFiguRE}: An Analytic Framework for {HDD}
Management",
journal =      j-TOMPECS,
volume =       "1",
number =       "3",
pages =        "10:1--10:27",
month =        may,
year =         "2016",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/2872331",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Jun 15 12:19:54 MDT 2017",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "http://dl.acm.org/citation.cfm?id=2872331",
abstract =     "Low disk drive utilization suggests that placing the
drive into a power saving mode during idle times may
decrease power consumption. We present PREFiguRE, a
robust framework that aims at harvesting future idle
intervals for power savings while meeting strict
quality constraints: first, it contains potential
delays in serving IO requests that occur during power
savings since the time to bring up the disk is not
negligible, and second, it ensures that the power
saving mechanism is triggered a few times only, such
that the disk wear-out due to powering up and down does
not compromise the disk's lifetime. PREFiguRE is based
on an analytic methodology that uses the histogram of
idle times to determine schedules for power saving
modes as a function of the preceding constraints.
PREFiguRE facilitates analysis for the evaluation of
the trade-offs between power savings and quality
targets for the current workload. Extensive
experimentation on a set of enterprise storage traces
illustrates PREFiguRE's effectiveness to consistently
achieve high power savings without undermining disk
reliability and performance.",
acknowledgement = ack-nhfb,
articleno =    "10",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Antunes:2016:EFD,
author =       "Nelson Antunes and Vladas Pipiras",
title =        "Estimation of Flow Distributions from Sampled
Traffic",
journal =      j-TOMPECS,
volume =       "1",
number =       "3",
pages =        "11:1--11:28",
month =        may,
year =         "2016",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/2891106",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Jun 15 12:19:54 MDT 2017",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "http://dl.acm.org/citation.cfm?id=2891106",
abstract =     "This work addresses the problem of estimating the
distributions of packet flow sizes and durations under
several methods of sampling packets. Two approaches,
one based on inversion and the other on asymptotics,
are considered. For the duration distribution, in
particular, both approaches require modeling the
structure of flows, with the duration distribution
being characterized in terms of the IATs (interarrival
times between packets) and size distributions of a
flow. The inversion of the flow IAT distribution from
sampled flow quantities, along with the inversion of
the flow size distribution (already used in the
literature) allows estimating the flow duration
distribution. Motivated by the limitations of the
inversion approach in estimating the distribution tails
for some sampling methods, an asymptotic approach is
developed to estimate directly the distribution tails
of flow durations and sizes from sampled quantities.
The adequacy of both approaches to estimate the flow
distributions is checked against two real Internet
traces.",
acknowledgement = ack-nhfb,
articleno =    "11",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Garetto:2016:UAP,
author =       "Michele Garetto and Emilio Leonardi and Valentina
Martina",
title =        "A Unified Approach to the Performance Analysis of
Caching Systems",
journal =      j-TOMPECS,
volume =       "1",
number =       "3",
pages =        "12:1--12:28",
month =        may,
year =         "2016",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/2896380",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Jun 15 12:19:54 MDT 2017",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "http://dl.acm.org/citation.cfm?id=2896380",
abstract =     "We propose a unified methodology to analyze the
performance of caches (both isolated and
interconnected), by extending and generalizing a
decoupling technique originally known as Che's
approximation, which provides very accurate results at
low computational cost. We consider several caching
policies (including a very attractive one, called k
-LRU), taking into account the effects of temporal
locality. In the case of interconnected caches, our
approach allows us to do better than the Poisson
approximation commonly adopted in prior work. Our
results, validated against simulations and trace-driven
experiments, provide interesting insights into the
performance of caching systems.",
acknowledgement = ack-nhfb,
articleno =    "12",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Xie:2016:DAI,
author =       "Hong Xie and John C. S. Lui and Don Towsley",
title =        "Design and Analysis of Incentive and Reputation
Mechanisms for Online Crowdsourcing Systems",
journal =      j-TOMPECS,
volume =       "1",
number =       "3",
pages =        "13:1--13:27",
month =        may,
year =         "2016",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/2897510",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Jun 15 12:19:54 MDT 2017",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "http://dl.acm.org/citation.cfm?id=2897510",
abstract =     "Today, online crowdsourcing services like Amazon
Mechanical Turk, UpWork, and Yahoo! Answers are gaining
in popularity. For such online services, it is
important to attract workers'' to provide
high-quality solutions to the tasks'' outsourced by
requesters.'' The challenge is that workers have
different skill sets and can provide different amounts
incentive and reputation mechanisms to solicit
high-quality solutions from workers. Our incentive
mechanism allows multiple workers to solve a task,
splits the reward among workers based on requester
evaluations of the solution quality, and guarantees
that high-skilled workers provide high-quality
solutions. However, our incentive mechanism suffers the
potential risk that a requester will eventually
collects low-quality solutions due to fundamental
limitations in task assigning accuracy. Our reputation
mechanism ensures that low-skilled workers do not
provide low-quality solutions by tracking workers'
historical contributions and penalizing those workers
having poor reputations. We show that by coupling our
reputation mechanism with our incentive mechanism, a
requester can collect at least one high-quality
solution. We present an optimization framework to
select parameters for our reputation mechanism. We show
that there is a trade-off between system efficiency
(i.e., the number of tasks that can be solved for a
given reward) and revenue (i.e., the amount of
transaction fees), and we present the optimal trade-off
curve between system efficiency and revenue. We
demonstrate the applicability and effectiveness of our
mechanisms through experiments using a real-world
dataset from UpWork. We infer model parameters from
this data, use them to determine proper rewards, and
select the parameters of our incentive and reputation
mechanisms for UpWork. Experimental results show that
our incentive and reputation mechanisms achieve 98.82\%
of the maximum system efficiency while only sacrificing
4\% of revenue.",
acknowledgement = ack-nhfb,
articleno =    "13",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

title =        "Scheduling Storms and Streams in the Cloud",
journal =      j-TOMPECS,
volume =       "1",
number =       "4",
pages =        "14:1--14:28",
month =        sep,
year =         "2016",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/2904080",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Jun 15 12:19:55 MDT 2017",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "http://dl.acm.org/citation.cfm?id=2904080",
abstract =     "Motivated by emerging big streaming data processing
we investigate the problem of scheduling graphs over a
large cluster of servers. Each graph is a job, where
nodes represent compute tasks and edges indicate data
flows between these compute tasks. Jobs (graphs) arrive
randomly over time and, upon completion, leave the
system. When a job arrives, the scheduler needs to
partition the graph and distribute it over the servers
to satisfy load balancing and cost considerations.
Specifically, neighboring compute tasks in the graph
that are mapped to different servers incur load on the
network; thus a mapping of the jobs among the servers
incurs a cost that is proportional to the number of
broken edges.'' We propose a low-complexity
randomized scheduling algorithm that, without service
preemptions, stabilizes the system with graph
arrivals/departures; more importantly, it allows a
smooth tradeoff between minimizing average partitioning
cost and average queue lengths. Interestingly, to avoid
service preemptions, our approach does not rely on a
Gibbs sampler; instead, we show that the corresponding
limiting invariant measure has an interpretation
stemming from a loss system.",
acknowledgement = ack-nhfb,
articleno =    "14",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

author =       "Alessandro Vittorio Papadopoulos and Ahmed Ali-Eldin
and Karl-Erik {\AA}rz{\'e}n and Johan Tordsson and Erik
Elmroth",
title =        "{PEAS}: A Performance Evaluation Framework for
Auto-Scaling Strategies in Cloud Applications",
journal =      j-TOMPECS,
volume =       "1",
number =       "4",
pages =        "15:1--15:31",
month =        sep,
year =         "2016",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/2930659",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Jun 15 12:19:55 MDT 2017",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "http://dl.acm.org/citation.cfm?id=2930659",
abstract =     "Numerous auto-scaling strategies have been proposed in
the past few years for improving various Quality of
Service (QoS) indicators of cloud applications, for
example, response time and throughput, by adapting the
amount of resources assigned to the application to meet
the workload demand. However, the evaluation of a
proposed auto-scaler is usually achieved through
experiments under specific conditions and seldom
includes extensive testing to account for uncertainties
in the workloads and unexpected behaviors of the
system. These tests by no means can provide guarantees
about the behavior of the system in general conditions.
framework for Auto-Scaling (PEAS) strategies in the
presence of uncertainties. The evaluation is formulated
as a chance constrained optimization problem, which is
solved using scenario theory. The adoption of such a
technique allows one to give probabilistic guarantees
of the obtainable performance. Six different
auto-scaling strategies have been selected from the
literature for extensive test evaluation and compared
using the proposed framework. We build a discrete event
simulator and parameterize it based on real
experiments. Using the simulator, each auto-scaler's
performance is evaluated using 796 distinct real
workload traces from projects hosted on the Wikimedia
foundations' servers, and their performance is compared
using PEAS. The evaluation is carried out using
different performance metrics, highlighting the
flexibility of the framework, while providing
probabilistic bounds on the evaluation and the
performance of the algorithms. Our results highlight
the problem of generalizing the conclusions of the
original published studies and show that based on the
evaluation criteria, a controller can be shown to be
better than other controllers.",
acknowledgement = ack-nhfb,
articleno =    "15",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Fricker:2016:AOS,
author =       "Christine Fricker and Fabrice Guillemin and Philippe
Robert and Guilherme Thompson",
the Framework of Fog Computing",
journal =      j-TOMPECS,
volume =       "1",
number =       "4",
pages =        "16:1--16:18",
month =        sep,
year =         "2016",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/2950047",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Jun 15 12:19:55 MDT 2017",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "http://dl.acm.org/citation.cfm?id=2950047",
abstract =     "In the context of fog computing, we consider a simple
case where data centers are installed at the edge of
the network and assume that if a request arrives at an
overloaded data center, then it is forwarded to a
neighboring data center with some probability. Data
centers are assumed to have a large number of servers,
and traffic at some of them is assumed to cause
saturation. In this case, the other data centers may
help to cope with this saturation regime by accepting
some of the rejected requests. Our aim is to
qualitatively estimate the gain achieved via
cooperation between neighboring data centers. After
proving some convergence results related to the scaling
limits of loss systems for the process describing the
number of free servers at both data centers, we show
that the performance of the system can be expressed in
terms of the invariant distribution of a random walk in
the quarter plane. By using and developing existing
results in the technical literature, explicit formulas
for the blocking rates of such a system are derived.",
acknowledgement = ack-nhfb,
articleno =    "16",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{DeCicco:2016:CTA,
author =       "Luca {De Cicco} and Yixi Gong and Dario Rossi and
Emilio Leonardi",
title =        "A Control-Theoretic Analysis of Low-Priority
Congestion Control Reprioritization under {AQM}",
journal =      j-TOMPECS,
volume =       "1",
number =       "4",
pages =        "17:1--17:33",
month =        sep,
year =         "2016",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/2934652",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Jun 15 12:19:55 MDT 2017",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "http://dl.acm.org/citation.cfm?id=2934652",
abstract =     "Recently, a negative interplay has been shown to arise
when scheduling/Active Queue Management (AQM)
techniques and low-priority congestion control
protocols are used together; namely, AQM resets the
relative level of priority among congestion control
protocols. This work explores this issue by carrying
out a control-theoretic analysis of the dynamical
system to prove some fundamental properties that fully
characterize the reprioritization phenomenon. In
particular, (i) we provide the closed-form solution of
the equilibrium in the open loop (i.e., fixing a target
loss probability p ); (ii) we provide a stability
analysis and a characterization of the reprioritization
phenomenon when closing the loop with AQM (i.e., that
dynamically adjusts the system loss probability). Our
results are important as the characterization of the
reprioritization phenomenon is not only quantitatively
accurate for the specific protocols and AQM considered
but also qualitatively accurate for a broader range of
congestion control protocol and AQM combinations.
Finally, while we find a sufficient condition to avoid
the reprioritization phenomenon, we also show, at the
same time, such conditions to be likely impractical:
Therefore, we propose a simple and practical
system-level solution that is able to reinstate
priorities among protocols.",
acknowledgement = ack-nhfb,
articleno =    "17",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Zhang:2016:TIM,
author =       "Linquan Zhang and Shaolei Ren and Chuan Wu and
Zongpeng Li",
title =        "A Truthful Incentive Mechanism for Emergency Demand
Response in Geo-Distributed Colocation Data Centers",
journal =      j-TOMPECS,
volume =       "1",
number =       "4",
pages =        "18:1--18:23",
month =        sep,
year =         "2016",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/2950046",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Jun 15 12:19:55 MDT 2017",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "http://dl.acm.org/citation.cfm?id=2950046",
abstract =     "Data centers are key participants in demand response
programs, including emergency demand response (EDR), in
which the grid coordinates consumers of large amounts
of electricity for demand reduction in emergency
situations to prevent major economic losses. While
existing literature concentrates on owner-operated data
centers, this work studies EDR in geo-distributed
multitenant colocation data centers in which servers
are owned and managed by individual tenants. EDR in
colocation data centers is significantly more
challenging due to lack of incentives to reduce energy
consumption by tenants who control their servers and
are typically on fixed power contracts with the
colocation operator. Consequently, to achieve demand
reduction goals set by the EDR program, the operator
has to rely on the highly expensive and/or
environmentally unfriendly on-site energy
backup/generation. To reduce cost and environmental
impact, an efficient incentive mechanism is therefore
needed, motivating tenants' voluntary energy reduction
in the case of EDR. This work proposes a novel
incentive mechanism, Truth-DR, which leverages a
reverse auction to provide monetary remuneration to
tenants according to their agreed energy reduction.
Truth-DR is computationally efficient, truthful, and
achieves 2-approximation in colocation-wide social
cost. Trace-driven simulations verify the efficacy of
the proposed auction mechanism.",
acknowledgement = ack-nhfb,
articleno =    "18",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Fu:2016:FPP,
author =       "Yongquan Fu and Ernst Biersack",
title =        "False-Positive Probability and Compression
Optimization for Tree-Structured {Bloom} Filters",
journal =      j-TOMPECS,
volume =       "1",
number =       "4",
pages =        "19:1--19:39",
month =        sep,
year =         "2016",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/2940324",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Jun 15 12:19:55 MDT 2017",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/datacompression.bib;
http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "http://dl.acm.org/citation.cfm?id=2940324",
abstract =     "Bloom filters are frequently used to to check the
membership of an item in a set. However, Bloom filters
face a dilemma: the transmission bandwidth and the
accuracy cannot be optimized simultaneously. This
dilemma is particularly severe for transmitting Bloom
filters to remote nodes when the network bandwidth is
limited. We propose a novel Bloom filter called
BloomTree that consists of a tree-structured
organization of smaller Bloom filters, each using a set
of independent hash functions. BloomTree spreads items
across levels that are compressed to reduce the
transmission bandwidth need. We show how to find
optimal configurations for BloomTree and investigate in
detail by how much BloomTree outperforms the standard
Bloom filter or the compressed Bloom filter. Finally,
we use the intersection of BloomTrees to predict the
set intersection, decreasing the false-positive
probabilities by several orders of magnitude compared
to both the compressed Bloom filter and the standard
Bloom filter.",
acknowledgement = ack-nhfb,
articleno =    "19",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Anonymous:2016:LR,
author =       "Anonymous",
title =        "List of Reviewers",
journal =      j-TOMPECS,
volume =       "1",
number =       "4",
pages =        "20:1--20:2",
month =        sep,
year =         "2016",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/2989212",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Jun 15 12:19:55 MDT 2017",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "http://dl.acm.org/citation.cfm?id=2989212",
acknowledgement = ack-nhfb,
articleno =    "20",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Molka:2016:CAW,
author =       "Karsten Molka and Giuliano Casale",
title =        "Contention-Aware Workload Placement for In-Memory
Databases in Cloud Environments",
journal =      j-TOMPECS,
volume =       "2",
number =       "1",
pages =        "1:1--1:29",
month =        nov,
year =         "2016",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/2961888",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Jun 15 12:19:55 MDT 2017",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "http://dl.acm.org/citation.cfm?id=2961888",
abstract =     "Big data processing is driven by new types of
performance modeling to efficiently optimize workload
placement for such systems. In particular, we propose
novel response time approximations for in-memory
databases based on fork-join queuing models and
contention probabilities to model variable threading
levels and per-class memory occupation under analytical
workloads. We combine these approximations with a
nonlinear optimization methodology that seeks optimal
load dispatching probabilities in order to minimize
memory swapping and resource utilization. We compare
our approach with state-of-the-art response time
approximations using real data from an SAP HANA
in-memory system and show that our models markedly
improve accuracy over existing approaches, at similar
computational costs.",
acknowledgement = ack-nhfb,
articleno =    "1",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Vergara:2016:FIC,
author =       "Ekhiotz Jon Vergara and Simin Nadjm-Tehrani and Mikael
Asplund",
title =        "Fairness and Incentive Considerations in Energy
Apportionment Policies",
journal =      j-TOMPECS,
volume =       "2",
number =       "1",
pages =        "2:1--2:29",
month =        nov,
year =         "2016",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/2970816",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Jun 15 12:19:55 MDT 2017",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "http://dl.acm.org/citation.cfm?id=2970816",
abstract =     "The energy consumption of a system is determined by
the system component usage patterns and interactions
between the coexisting entities and resources. Energy
accounting plays an essential role in revealing the
contribution of each entity to the total consumption
and for energy management. Unfortunately, energy
accounting inherits the apportionment problem of
accounting in general, which does not have a general
cooperative game theory, which is commonly used in cost
allocation problems to study the energy apportionment
problem, that is, the problem of prescribing the actual
energy consumption of a system to the consuming
entities (e.g., applications, processes, or users of
the system). We identify five relevant fairness
properties for energy apportionment and present a
detailed categorisation and analysis of eight
previously proposed energy apportionment policies from
different fields in computer and communication systems.
In addition, we propose two novel energy apportionment
policies based on cooperative game theory that provide
strong fairness notion and a rich incentive structure.
Our comparative analysis in terms of the identified
five fairness properties as well as information
requirement and computational complexity shows that
there is a tradeoff between fairness and the other
evaluation criteria. We provide guidelines to select an
energy apportionment policy depending on the purpose of
the apportionment and the characteristics of the
system.",
acknowledgement = ack-nhfb,
articleno =    "2",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Nasiriani:2016:FAC,
author =       "Neda Nasiriani and Cheng Wang and George Kesidis and
Bhuvan Urgaonkar and Lydia Y. Chen and Robert Birke",
title =        "On Fair Attribution of Costs Under Peak-Based Pricing
to Cloud Tenants",
journal =      j-TOMPECS,
volume =       "2",
number =       "1",
pages =        "3:1--3:28",
month =        nov,
year =         "2016",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/2970815",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Jun 15 12:19:55 MDT 2017",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "http://dl.acm.org/citation.cfm?id=2970815",
abstract =     "The costs incurred by cloud providers towards
operating their data centers are often determined in
large part by their peak demands. The pricing schemes
currently used by cloud providers to recoup these costs
from their tenants, however, do not distinguish tenants
based on their contributions to the cloud's overall
peak demand. Using the concrete example of peak-based
pricing as employed by many electric utility companies,
we show that this gap'' may lead to unfair
attribution of costs to the tenants. Simple
enhancements of existing cloud pricing (e.g., analogous
to the coincident peak pricing (CPP) used by some
shortcomings and suffer from short-term unfairness and
undesirable oscillatory price-vs.-demand relationships
offered to tenants. To overcome these shortcomings, we
define an alternative pricing scheme to more fairly
distribute a cloud's costs among its tenants. We
demonstrate the efficacy of our scheme under
price-sensitive tenant demand response using a
combination of (i) extensive empirical evaluation with
recent workloads from commercial data centers operated
by IBM and (ii) analytical [modeling] through
non-cooperative game theory for a special case of
tenant demand model.",
acknowledgement = ack-nhfb,
articleno =    "3",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Nain:2016:FDD,
author =       "Philippe Nain and Don Towsley",
title =        "File Dissemination in Dynamic Graphs: The Case of
Independent and Correlated Links in Series",
journal =      j-TOMPECS,
volume =       "2",
number =       "1",
pages =        "4:1--4:23",
month =        nov,
year =         "2016",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/2981344",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Jun 15 12:19:55 MDT 2017",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "http://dl.acm.org/citation.cfm?id=2981344",
a file across N communication links subject to
stochastic changes in the sending rate of each link.
Each link's sending rate is modeled by a finite-state
Markov process. Two cases, one where links evolve
independently of one another ( N mutually independent
Markov processes) and the second where their behaviors
are dependent (these N Markov processes are not
mutually independent), are considered. A particular
instance where the above is encountered is ad hoc
delay/tolerant networks where links are subject to
intermittent unavailability.",
acknowledgement = ack-nhfb,
articleno =    "4",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Liu:2016:SSS,
author =       "Qingyun Liu and Xiaohan Zhao and Walter Willinger and
Xiao Wang and Ben Y. Zhao and Haitao Zheng",
title =        "Self-Similarity in Social Network Dynamics",
journal =      j-TOMPECS,
volume =       "2",
number =       "1",
pages =        "5:1--5:26",
month =        nov,
year =         "2016",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/2994142",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Jun 15 12:19:55 MDT 2017",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "http://dl.acm.org/citation.cfm?id=2994142",
abstract =     "Analyzing and modeling social network dynamics are key
to accurately predicting resource needs and system
behavior in online social networks. The presence of
statistical scaling properties, that is,
self-similarity, is critical for determining how to
model network dynamics. In this work, we study the role
that self-similarity scaling plays in a social network
edge creation (that is, links created between users)
process, through analysis of two detailed, time-stamped
traces, a 199 million edge trace over 2 years in the
Renren social network, and 876K interactions in a
4-year trace of Facebook. Using wavelet-based analysis,
we find that the edge creation process in both networks
is consistent with self-similarity scaling, once we
account for periodic user activity that makes edge
creation process non-stationary. Using these findings,
we build a complete model of social network dynamics
that combines temporal and spatial components.
Specifically, the temporal behavior of our model
reflects self-similar scaling properties, and accounts
for certain deterministic non-stationary features. The
spatial side accounts for observed long-term graph
properties, such as graph distance shrinkage and local
declustering. We validate our model against network
dynamics in Renren and Facebook datasets, and show that
it succeeds in producing desired properties in both
temporal patterns and graph structural features.",
acknowledgement = ack-nhfb,
articleno =    "5",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Krishnasamy:2016:DSR,
author =       "Subhashini Krishnasamy and Rajat Sen and Sanjay
Shakkottai and Sewoong Oh",
journal =      j-TOMPECS,
volume =       "2",
number =       "1",
pages =        "6:1--6:29",
month =        nov,
year =         "2016",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/2988543",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Jun 15 12:19:55 MDT 2017",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "http://dl.acm.org/citation.cfm?id=2988543",
abstract =     "With the vast number of items, Web pages, and news
from which to choose, online services and customers
both benefit tremendously from personalized recommender
systems. Such systems additionally provide great
ads alongside genuine recommendations. We consider a
biased recommendation system in which such ads are
displayed without any tags (disguised as genuine
recommendations), rendering them indistinguishable to a
single user. We ask whether it is possible for a small
subset of collaborating users to detect such bias. We
propose an algorithm that can detect this type of bias
through statistical analysis on the collaborating
users' feedback. The algorithm requires only binary
information indicating whether a user was satisfied
with each of the recommended item or not. This makes
the algorithm widely appealing to real-world issues
such as identification of search engine bias and
pharmaceutical lobbying. We prove that the proposed
algorithm detects the bias with high probability for a
broad class of recommendation systems when a sufficient
number of users provides feedback on a sufficient
number of recommendations. We provide extensive
simulations with real datasets and practical
recommender systems, which confirm the trade-offs in
the theoretical guarantees.",
acknowledgement = ack-nhfb,
articleno =    "6",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Petsas:2017:MMA,
author =       "Thanasis Petsas and Antonis Papadogiannakis and
Michalis Polychronakis and Evangelos P. Markatos and
Thomas Karagiannis",
title =        "Measurement, Modeling, and Analysis of the Mobile App
Ecosystem",
journal =      j-TOMPECS,
volume =       "2",
number =       "2",
pages =        "7:1--7:33",
month =        may,
year =         "2017",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/2993419",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Jun 15 12:19:56 MDT 2017",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "http://dl.acm.org/citation.cfm?id=2993419",
abstract =     "Mobile applications (apps) have been gaining
popularity due to the advances in mobile technologies
and the large increase in the number of mobile users.
Consequently, several app distribution platforms, which
updating software applications in modern mobile
devices, have recently emerged. To better understand
development strategies in this rapidly evolving mobile
app ecosystem, we systematically monitored and analyzed
four popular third-party Android app marketplaces. Our
study focuses on measuring, analyzing, and modeling the
app popularity distribution and explores how pricing
and revenue strategies affect app popularity and
developers' income. Our results indicate that unlike
web and peer-to-peer file sharing workloads, the app
popularity distribution deviates from commonly observed
Zipf-like models. We verify that these deviations can
we refer to as the clustering effect. We validate the
existence of this effect by revealing a strong temporal
these observations, we propose a new formal clustering
demonstrate that it closely fits measured data.
Moreover, we observe that paid apps follow a different
popularity distribution than free apps and show how
free apps with an ad-based revenue strategy may result
in higher financial benefits than paid apps. We believe
that this study can be useful to appstore designers for
improving content delivery and recommendation systems,
as well as to app developers for selecting proper
pricing policies to increase their income.",
acknowledgement = ack-nhfb,
articleno =    "7",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Yi:2017:CDC,
author =       "Xiaomeng Yi and Fangming Liu and Di Niu and Hai Jin
and John C. S. Lui",
title =        "{Cocoa}: Dynamic Container-Based Group Buying
Strategies for Cloud Computing",
journal =      j-TOMPECS,
volume =       "2",
number =       "2",
pages =        "8:1--8:31",
month =        may,
year =         "2017",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3022876",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Jun 15 12:19:56 MDT 2017",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib;
http://www.math.utah.edu/pub/tex/bib/virtual-machines.bib",
URL =          "http://dl.acm.org/citation.cfm?id=3022876",
abstract =     "Although the Infrastructure-as-a-Service (IaaS) cloud
offers diverse instance types to users, a significant
portion of cloud users, especially those with small and
short demands, cannot find an instance type that
exactly fits their needs or fully utilize purchased
instance-hours. In the meantime, cloud service
providers are also faced with the challenge to
consolidate small, short jobs, which exhibit strong
dynamics, to effectively improve resource utilization.
To handle such inefficiencies and improve cloud
resource utilization, we propose Cocoa (COmputing in
COntAiners), a novel group buying mechanism that
organizes jobs with complementary resource demands into
groups and allocates them to group buying deals
predefined by cloud providers. Each group buying deal
offers a resource pool for all the jobs in the deal,
which can be implemented as either a virtual machine or
a physical server. By running each user job on a
virtualized container, our mechanism allows flexible
resource sharing among different users in the same
group buying deal, while improving resource utilization
for cloud providers. To organize jobs with varied
resource demands and durations into groups, we model
the initial static group organization as a
variable-sized vector bin packing problem, and the
subsequent dynamic group organization problem as an
online multidimensional knapsack problem. Through
extensive simulations driven by a large amount of real
usage traces from a Google cluster, we evaluate the
potential cost reduction achieved by Cocoa. We show
that through the effective combination and interaction
of the proposed static and dynamic group organization
strategies, Cocoa greatly outperforms the existing
the feasibility of group buying in cloud computing.",
acknowledgement = ack-nhfb,
articleno =    "8",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Liu:2017:ECE,
author =       "Yu-Hang Liu and Xian-He Sun",
title =        "Evaluating the Combined Effect of Memory Capacity and
Concurrency for Many-Core Chip Design",
journal =      j-TOMPECS,
volume =       "2",
number =       "2",
pages =        "9:1--9:25",
month =        may,
year =         "2017",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3038915",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Jun 15 12:19:56 MDT 2017",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "http://dl.acm.org/citation.cfm?id=3038915",
abstract =     "Modern memory systems are structured under hierarchy
and concurrency. The combined impact of hierarchy and
concurrency, however, is application dependent and
2 -Bound, a data-driven analytical model that serves
the purpose of optimizing many-core design. C 2 -Bound
considers both memory capacity and data access
concurrency. It utilizes the combined power of the
newly proposed latency model, concurrent average memory
access time, and the well-known memory-bounded speedup
model (Sun-Ni's law) to facilitate computing tasks.
Compared to traditional chip designs that lack the
notion of memory capacity and concurrency, the C 2
-Bound model finds that memory bound factors
significantly impact the optimal number of cores as
well as their optimal silicon area allocations,
especially for data-intensive applications with a
non-parallelizable sequential portion. Therefore, our
model is valuable to the design of next-generation
many-core architectures that target big data
processing, where working sets are usually larger than
the conventional scientific computing. These findings
are evidenced by our detailed simulations, which show,
with C 2 -Bound, the design space of chip design can be
narrowed down significantly up to four orders of
magnitude. C 2 -Bound analytic results can be either
used in reconfigurable hardware environments or, by
software designers, applied to scheduling,
partitioning, and allocating resources among diverse
applications.",
acknowledgement = ack-nhfb,
articleno =    "9",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Simhon:2017:ARG,
author =       "Eran Simhon and David Starobinski",
journal =      j-TOMPECS,
volume =       "2",
number =       "2",
pages =        "10:1--10:21",
month =        may,
year =         "2017",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3053046",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Jun 15 12:19:56 MDT 2017",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "http://dl.acm.org/citation.cfm?id=3053046",
abstract =     "Advance reservation (AR) services form a pillar of
several branches of the economy, including
transportation, lodging, dining, and, more recently,
cloud computing. In this work, we use game theory to
analyze a slotted AR system in which customers differ
in their lead times. For each given time slot, the
number of customers requesting service is a random
variable following a general probability distribution.
Based on statistical information, the customers decide
whether or not to make an advance reservation of server
resources in future slots for a fee. We prove that only
two types of equilibria are possible: either none of
the customers makes AR or only customers with lead time
greater than some threshold make AR. Our analysis
further shows that the fee that maximizes the
provider's profit may lead to other equilibria, one of
which yields zero profit. In order to prevent ending up
with no profit, the provider can elect to advertise a
lower fee yielding a guaranteed but smaller profit. We
refer to the ratio of the maximum possible profit to
the maximum guaranteed profit as the price of
conservatism. When the number of customers is a Poisson
random variable, we prove that the price of
conservatism is one in the single-server case, but can
be arbitrarily high in a many-server system.",
acknowledgement = ack-nhfb,
articleno =    "10",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Kelley:2017:OMA,
author =       "Jaimie Kelley and Christopher Stewart and Nathaniel
Morris and Devesh Tiwari and Yuxiong He and Sameh
Elnikety",
title =        "Obtaining and Managing Answer Quality for Online
Data-Intensive Services",
journal =      j-TOMPECS,
volume =       "2",
number =       "2",
pages =        "11:1--11:31",
month =        may,
year =         "2017",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3055280",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Jun 15 12:19:56 MDT 2017",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "http://dl.acm.org/citation.cfm?id=3055280",
abstract =     "Online data-intensive (OLDI) services use anytime
algorithms to compute over large amounts of data and
respond quickly. Interactive response times are a
priority, so OLDI services parallelize query execution
across distributed software components and return best
effort answers based on the data so far processed.
Omitted data from slow components could lead to better
could be is difficult. We propose Ubora, a design
approach to measure the effect of slow-running
components on the quality of answers. Ubora randomly
samples online queries and executes them a second time.
The first online execution omits data from slow
components and provides interactive answers. The second
execution uses mature results from intermediate
components completed after the online execution
finishes. Ubora uses memoization to speed up mature
executions by replaying network messages exchanged
between components. Our systems-level implementation
works for a wide range of services, including
Hadoop/Yarn, Apache Lucene, the EasyRec Recommendation
Engine, and the OpenEphyra question-answering system.
Ubora computes answer quality with more mature
executions per second than competing approaches that do
not use memoization. With Ubora, we show that answer
quality is effective at guiding online admission
control. While achieving the same answer quality on
higher peak throughput on low-priority queries than a
competing controller guided by the rate of timeouts.",
acknowledgement = ack-nhfb,
articleno =    "11",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Joshi:2017:ERT,
author =       "Gauri Joshi and Emina Soljanin and Gregory Wornell",
title =        "Efficient Redundancy Techniques for Latency Reduction
in Cloud Systems",
journal =      j-TOMPECS,
volume =       "2",
number =       "2",
pages =        "12:1--12:30",
month =        may,
year =         "2017",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3055281",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Jun 15 12:19:56 MDT 2017",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "http://dl.acm.org/citation.cfm?id=3055281",
abstract =     "In cloud computing systems, assigning a task to
multiple servers and waiting for the earliest copy to
finish is an effective method to combat the variability
in response time of individual servers and reduce
latency. But adding redundancy may result in higher
cost of computing resources, as well as an increase in
queueing delay due to higher traffic load. This work
helps in understanding when and how redundancy gives a
cost-efficient reduction in latency. For a general task
service time distribution, we compare different
redundancy strategies in terms of the number of
redundant tasks and the time when they are issued and
canceled. We get the insight that the log-concavity of
the task service time creates a dichotomy of when
adding redundancy helps. If the service time
distribution is log-convex (i.e., log of the tail
probability is convex), then adding maximum redundancy
reduces both latency and cost. And if it is log-concave
(i.e., log of the tail probability is concave), then
less redundancy, and early cancellation of redundant
tasks is more effective. Using these insights, we
design a general redundancy strategy that achieves a
good latency-cost trade-off for an arbitrary service
time distribution. This work also generalizes and
extends some results in the analysis of fork-join
queues.",
acknowledgement = ack-nhfb,
articleno =    "12",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Du:2017:SCB,
author =       "Yuhuan Du and Gustavo {De Veciana}",
title =        "Scheduling for Cloud-Based Computing Systems to
Support Soft Real-Time Applications",
journal =      j-TOMPECS,
volume =       "2",
number =       "3",
pages =        "13:1--13:??",
month =        sep,
year =         "2017",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3063713",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:14 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3063713",
abstract =     "Cloud-based computing infrastructure provides an
efficient means to support real-time processing
workloads, for example, virtualized base station
processing, and collaborative video conferencing. This
article addresses resource allocation for a computing
system with multiple resources supporting heterogeneous
soft real-time applications subject to Quality of
Service (QoS) constraints on failures to meet
processing deadlines. We develop a general outer bound
on the feasible QoS region for non-clairvoyant resource
allocation policies and an inner bound for a natural
class of policies based on dynamically prioritizing
applications' tasks by favoring those with the largest
(QoS) deficits. This provides an avenue to study the
efficiency of two natural resource allocation policies:
(1) priority-based greedy task scheduling for
applications with variable workloads and (2)
priority-based task selection and optimal scheduling
for applications with deterministic workloads. The
near-optimality of these simple policies emerges when
when the number of compute resources is large. Analysis
and simulations show substantial resource savings for
such policies over reservation-based designs.",
acknowledgement = ack-nhfb,
articleno =    "13",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Dong:2017:CAT,
author =       "Fang Dong and Kui Wu and Venkatesh Srinivasan",
title =        "Copula Analysis of Temporal Dependence Structure in
{Markov} Modulated {Poisson} Process and Its
Applications",
journal =      j-TOMPECS,
volume =       "2",
number =       "3",
pages =        "14:1--14:??",
month =        sep,
year =         "2017",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3089254",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:14 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3089254",
abstract =     "The Markov Modulated Poisson Process (MMPP) has been
extensively studied in random process theory and widely
applied in various applications involving Poisson
arrivals whose rate varies following a Markov process.
Despite the rich literature on MMPP, very little is
known on its intricate temporal dependence structure.
No exact solution is available so far to capture the
functional temporal dependence of MMPP at the
tackles the above challenges with copula analysis. It
not only presents a novel analytical framework to
capture the temporal dependence of MMPP but also
provides the exact copula-based solutions for single
MMPP as well as the aggregate of independent MMPP. This
theoretical contribution discloses functional
dependence structure of MMPP. It also lays the
foundation for many applications that rely on the
temporal dependence of MMPP for adaptive control or
predictive resource provisioning. We demonstrate case
studies, with real-world trace data as well as
simulation, to illustrate the practical significance of
our analytical results.",
acknowledgement = ack-nhfb,
articleno =    "14",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Ferragut:2017:CVC,
author =       "Andres Ferragut and Fernando Paganini and Adam
Wierman",
title =        "Controlling the Variability of Capacity Allocations
Using Service Deferrals",
journal =      j-TOMPECS,
volume =       "2",
number =       "3",
pages =        "15:1--15:??",
month =        sep,
year =         "2017",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3086506",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:14 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3086506",
abstract =     "Ensuring predictability is a crucial goal for service
systems. Traditionally, research has focused on
designing systems that ensure predictable performance
for service requests. Motivated by applications in
focuses on a different form of predictability:
predictable allocations of service capacity. The focus
of the article is a new model where service capacity
can be scaled dynamically and service deferrals
(subject to deadline constraints) can be used to
control the variability of the active service capacity.
Four natural policies for the joint problem of
scheduling and managing the active service capacity are
considered. For each, the variability of service
capacity and the likelihood of deadline misses are
derived. Further, the paper illustrates how pricing can
be used to provide incentives for jobs to reveal
deadlines and thus enable the possibility of service
deferral in systems where the flexibility of jobs is
not known to the system a priori.",
acknowledgement = ack-nhfb,
articleno =    "15",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Li:2017:IPO,
author =       "Hao Li and Xinhai Xu and Miao Wang and Chao Li and
Xiaoguang Ren and Xuejun Yang",
title =        "Insertion of {PETSc} in the {OpenFOAM} Framework",
journal =      j-TOMPECS,
volume =       "2",
number =       "3",
pages =        "16:1--16:??",
month =        sep,
year =         "2017",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3098821",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:14 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3098821",
abstract =     "OpenFOAM is a widely used open source framework for
simulation in several areas of computational fluid
dynamics and engineering. As a partial differential
equation (PDE)-based framework, OpenFOAM suffers from a
performance bottleneck in solving large-scale sparse
linear systems of equations. To address the problem,
by inserting PETSc, a dedicated numerical solving
package, into the OpenFOAM to speed up the process of
solving linear equation systems. The design of the
OpenFOAM-PETSc framework is described, and the
implementation of an efficient matrix conversion
algorithm is given as a case study. Validation tests on
a high-performance computing cluster show that
OpenFOAM-PETSc reduces the time of solving PDEs by
about 27\% in the lid-driven cavity flow case and by
more than 50\% in flow around the cylinder case in
comparison with OpenFOAM, without compromising the
preliminary performance analysis of different numerical
solution methods, which may provide guidelines for
further optimizations.",
acknowledgement = ack-nhfb,
articleno =    "16",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Liu:2017:FPE,
author =       "Yanpei Liu and Guilherme Cox and Qingyuan Deng and
Stark C. Draper and Ricardo Bianchini",
title =        "Fast Power and Energy Management for Future Many-Core
Systems",
journal =      j-TOMPECS,
volume =       "2",
number =       "3",
pages =        "17:1--17:??",
month =        sep,
year =         "2017",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3086504",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:14 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3086504",
abstract =     "Future servers will incorporate many active low-power
modes for each core and for the main memory subsystem.
Though these modes provide flexibility for power and/or
energy management via Dynamic Voltage and Frequency
Scaling (DVFS), prior work has shown that they must be
managed in a coordinated manner. This requirement
creates a combinatorial space of possible power mode
configurations. As a result, it becomes increasingly
challenging to quickly select the configuration that
optimizes for both performance and power/energy
model for working with the abundant active low-power
modes in many-core systems. Based on the queuing model,
we derive two fast algorithms that optimize for
performance and efficiency using both CPU and memory
DVFS. Our first algorithm, called FastCap, maximizes
the performance of applications under a full-system
power cap, while promoting fairness across
applications. Our second algorithm, called FastEnergy,
maximizes the full-system energy savings under
predefined application performance loss bounds. Both
FastCap and FastEnergy operate online and efficiently,
using a small set of performance counters as input. To
evaluate them, we simulate both algorithms for a
many-core server running different types of workloads.
Our results show that FastCap achieves better
application performance and fairness than prior power
capping techniques for the same power budget, whereas
FastEnergy conserves more energy than prior energy
management techniques for the same performance
constraint. FastCap and FastEnergy together demonstrate
the applicability of the queuing model for managing the
abundant active low-power modes in many-core systems.",
acknowledgement = ack-nhfb,
articleno =    "17",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Anselmi:2017:EC,
author =       "Jonatha Anselmi and Danilo Ardagna and John C. S. Lui
and Adam Wierman and Yunjian Xu and Zichao Yang",
title =        "The Economics of the Cloud",
journal =      j-TOMPECS,
volume =       "2",
number =       "4",
pages =        "18:1--18:??",
month =        dec,
year =         "2017",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3086574",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:15 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3086574",
of price competition and congestion in the cloud
computing marketplace. Specifically, we propose a
three-tier market model that captures a marketplace
Software-as-a-Service (SaaS) providers, which in turn
purchase computing resources from either
Provider-as-a-Service (PaaS) or
Infrastructure-as-a-Service (IaaS) providers. Within
each level, we define and characterize market
equilibria. Further, we use these characterizations to
understand the relative profitability of SaaSs and
PaaSs/IaaSs and to understand the impact of price
competition on the user experienced performance, that
is, the price of anarchy'' of the cloud marketplace.
Our results highlight that both of these depend
fundamentally on the degree to which congestion results
from shared or dedicated resources in the cloud.",
acknowledgement = ack-nhfb,
articleno =    "18",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Niu:2017:RAS,
author =       "Di Niu and Hong Xu and Baochun Li",
title =        "Resource Auto-Scaling and Sparse Content Replication
for Video Storage Systems",
journal =      j-TOMPECS,
volume =       "2",
number =       "4",
pages =        "19:1--19:??",
month =        dec,
year =         "2017",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3079045",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:15 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3079045",
abstract =     "Many video-on-demand (VoD) providers are relying on
public cloud providers for video storage, access, and
a VoD provider may make optimal bandwidth reservations
from a cloud service provider to guarantee the
streaming performance while paying for the bandwidth,
storage, and transfer costs. We propose a predictive
resource auto-scaling system that dynamically books the
minimum amount of bandwidth resources from multiple
servers in a cloud storage system to allow the VoD
provider to match its short-term demand projections. We
exploit the anti-correlation between the demands of
different videos for statistical multiplexing to hedge
the risk of under-provisioning. The optimal load
direction from video channels to cloud servers without
replication constraints is derived with provable
performance. We further study the joint load direction
and sparse content placement problem that aims to
reduce bandwidth reservation cost under sparse content
replication requirements. We propose several
algorithms, and especially an iterative L 1 -norm
penalized optimization procedure, to efficiently solve
the problem while effectively limiting the video
migration overhead. The proposed system is backed up by
a demand predictor that forecasts the expectation,
volatility, and correlation of the streaming traffic
associated with different videos based on statistical
learning. Extensive simulations are conducted to
evaluate our proposed algorithms, driven by the
real-world workload traces collected from a commercial
VoD system.",
acknowledgement = ack-nhfb,
articleno =    "19",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Alves:2017:BMI,
author =       "Renan C. A. Alves and C{\'\i}ntia B. Margi",
title =        "Behavioral Model of {IEEE 802.15.4} Beacon-Enabled
Mode Based on Colored {Petri} Net",
journal =      j-TOMPECS,
volume =       "2",
number =       "4",
pages =        "20:1--20:??",
month =        dec,
year =         "2017",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3115389",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:15 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3115389",
abstract =     "The IEEE 802.15.4 standard is widely employed in
power-constrained scenarios, such as Wireless Sensor
Networks deployments. Therefore, modeling this standard
is useful to predict network performance and fine-tune
parameter settings. Previous work rely on determining
all reachable network states, usually by a Markov
chain, which is often complex and error prone. In
contrast, we provide a novel behavioral approach to the
IEEE 802.15.4 modeling, which covers the literature gap
in assessing all metrics of interest, modeling
asymmetric traffic condition and fully comprising the
beacon-enabled mode. In addition, it is possible to
test different values for the parameters of the
standard, such as aMaxFrameRetries, macMaxCSMABackoffs,
initialCW, and aUnitBackoffPeriod. The model was
validated by NS2 simulations and by a testbed composed
of telosB motes.",
acknowledgement = ack-nhfb,
articleno =    "20",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Neglia:2017:ATA,
author =       "Giovanni Neglia and Damiano Carra and Mingdong Feng
and Vaishnav Janardhan and Pietro Michiardi and Dimitra
Tsigkari",
title =        "Access-Time-Aware Cache Algorithms",
journal =      j-TOMPECS,
volume =       "2",
number =       "4",
pages =        "21:1--21:??",
month =        dec,
year =         "2017",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3149001",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:15 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3149001",
abstract =     "Most of the caching algorithms are oblivious to
requests' timescale, but caching systems are capacity
constrained and, in practical cases, the hit rate may
be limited by the cache's impossibility to serve
requests fast enough. In particular, the hard-disk
access time can be the key factor capping cache
replacement policy that takes advantage of a
hierarchical caching architecture, and in particular of
access-time difference between memory and disk. Our
policy is optimal when requests follow the independent
reference model and significantly reduces the hard-disk
load, as shown also by our realistic, trace-driven
evaluation. Moreover, we show that our policy can be
considered in a more general context, since it can be
easily adapted to minimize any retrieval cost, as far
as costs add over cache misses.",
acknowledgement = ack-nhfb,
articleno =    "21",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Biondi:2017:WYL,
author =       "Elisabetta Biondi and Chiara Boldrini and Andrea
Passarella and Marco Conti",
title =        "What You Lose When You Snooze: How Duty Cycling
Impacts on the Contact Process in Opportunistic
Networks",
journal =      j-TOMPECS,
volume =       "2",
number =       "4",
pages =        "22:1--22:??",
month =        dec,
year =         "2017",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3149007",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:15 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3149007",
abstract =     "In opportunistic networks, putting devices in
energy-saving mode is crucial to preserve their
battery, and hence to increase the lifetime of the
network and foster user participation. A popular
strategy for energy saving is duty cycling. However,
when in energy-saving mode, users cannot communicate
with each other. The side effects of duty cycling are
twofold. On the one hand, duty cycling may reduce the
number of usable contacts for delivering messages,
increasing intercontact times, and delays. On the other
hand, duty cycling may break long contacts into smaller
contacts, thus also reducing the capacity of the
opportunistic network. Despite the potential serious
effects, the role played by duty cycling in
opportunistic networks has been often neglected in the
we propose a general model for deriving the pairwise
contact and intercontact times measured when a duty
cycling policy is superimposed on the original
encounter process determined only by node mobility. The
model we propose is general, i.e., not bound to a
specific distribution of contact and intercontact
times, and very accurate, as we show exploiting two
traces of real human mobility for validation. Using
this model, we derive several interesting results about
the properties of measured contact and intercontact
times with duty cycling: their distribution, how their
coefficient of variation changes depending on the duty
cycle value, and how the duty cycling affects the
capacity and delay of an opportunistic network. The
applicability of these results is broad, ranging from
performance models for opportunistic networks that
factor in the duty cycling effect, to the optimisation
of the duty cycle to meet a certain target
performance.",
acknowledgement = ack-nhfb,
articleno =    "22",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Abouzeid:2017:LR,
author =       "Alhussein Abouzeid",
title =        "List of Reviewers",
journal =      j-TOMPECS,
volume =       "2",
number =       "4",
pages =        "23:1--23:??",
month =        dec,
year =         "2017",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3162084",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:15 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3162084",
acknowledgement = ack-nhfb,
articleno =    "23",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Liu:2018:BGB,
author =       "Chubo Liu and Kenli Li and Zhuo Tang and Keqin Li",
title =        "Bargaining Game-Based Scheduling for Performance
Guarantees in Cloud Computing",
journal =      j-TOMPECS,
volume =       "3",
number =       "1",
pages =        "1:1--1:??",
month =        feb,
year =         "2018",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3141233",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:15 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3141233",
performance guarantees of all users in cloud computing.
Each cloud user submits requests with average response
time requirement, and the cloud provider tries to find
a scheduling scheme, i.e., allocating user requests to
limited servers, such that the average response times
of all cloud users can be guaranteed. We formulate the
considered scenario into a cooperative game among
multiple users and try to find a Nash bargaining
solution (NBS), which can simultaneously satisfy all
users' performance demands. We first prove the
existence of NBS and then analyze its computation.
Specifically, for the situation when all allocating
substreams are strictly positive, we propose a
computational algorithm ( CA ), which can find the NBS
very efficiently. For the more general case, we propose
an iterative algorithm ( IA ), which is based on
duality theory. The convergence of our proposed IA
algorithm is also analyzed. Finally, we conduct some
numerical calculations. The experimental results show
that our IA algorithm can find an appropriate
scheduling strategy and converges to a stable state
very quickly.",
acknowledgement = ack-nhfb,
articleno =    "1",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Nordio:2018:STQ,
author =       "Alessandro Nordio and Alberto Tarable and Emilio
Leonardi and Marco Ajmone Marsan",
title =        "Selecting the Top-Quality Item Through Crowd Scoring",
journal =      j-TOMPECS,
volume =       "3",
number =       "1",
pages =        "2:1--2:??",
month =        feb,
year =         "2018",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3157736",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:15 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3157736",
abstract =     "We investigate crowdsourcing algorithms for finding
the top-quality item within a large collection of
objects with unknown intrinsic quality values. This is
an important problem with many relevant applications,
such as in networked recommendation systems. The core
of the algorithms is that objects are distributed to
crowd workers, who return a noisy and biased
evaluation. All received evaluations are then combined
to identify the top-quality object. We first present a
simple probabilistic model for the system under
investigation. Then we devise and study a class of
way objects to workers. We compare the performance of
several algorithms, which correspond to different
choices of the design parameters/metrics. In the
simulations, we show that some of the algorithms
achieve near optimal performance for a suitable setting
of the system parameters.",
acknowledgement = ack-nhfb,
articleno =    "2",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Li:2018:MFA,
author =       "Bin Li and Aditya Ramamoorthy and R. Srikant",
title =        "Mean-Field Analysis of Coding Versus Replication in
Large Data Storage Systems",
journal =      j-TOMPECS,
volume =       "3",
number =       "1",
pages =        "3:1--3:??",
month =        feb,
year =         "2018",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3159172",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:15 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3159172",
abstract =     "We study cloud storage systems with a very large
number of files stored in a very large number of
servers. In such systems, files are either replicated
or coded to ensure reliability, i.e., to guarantee file
recovery from server failures. This redundancy in
storage can further be exploited to improve system
performance (mean file-access delay) through
it is unclear whether coding or replication is better
from a system performance perspective since the
corresponding queueing analysis of such systems is, in
general, quite difficult except for the trivial case
when the system load asymptotically tends to zero.
Here, we study the more difficult case where the system
load is not asymptotically zero. Using the fact that
the system size is large, we obtain a mean-field limit
for the steady-state distribution of the number of file
access requests waiting at each server. We then use the
mean-field limit to show that, for a given storage
capacity per file, coding strictly outperforms
replication at all traffic loads while improving
reliability. Further, the factor by which the
performance improves in the heavy traffic is at least
as large as in the light-traffic case. Finally, we
validate these results through extensive simulations.",
acknowledgement = ack-nhfb,
articleno =    "3",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Chu:2018:EQC,
author =       "Cing-Yu Chu and Shannon Chen and Yu-Chuan Yen and
Su-Ling Yeh and Hao-Hua Chu and Polly Huang",
title =        "{EQ}: A {QoE}-Centric Rate Control Mechanism for
{VoIP} Calls",
journal =      j-TOMPECS,
volume =       "3",
number =       "1",
pages =        "4:1--4:??",
month =        feb,
year =         "2018",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3170430",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:15 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3170430",
abstract =     "The rising popularity of data calls and the slowed
global economy have posed a challenge to voice data
networking-how to satisfy the growing user demand for
VoIP calls under limited network resources. In a
bandwidth-constrained network in particular, raising
the bitrate for one call implies a lowered bitrate for
another. Therefore, knowing whether it is worthwhile to
raise one call's bitrate while other users might
complain is crucial to the design of a user-centric
rate control mechanism. To this end, previous work
(Chen et al. 2012) has reported a log-like relationship
between bitrate and user experience (i.e., QoE) in
Skype calls. To show that the relationship extends to
more general VoIP calls, we conduct a 60-participant
user study via the Amazon Mechanical Turk crowdsourcing
platform and reaffirm the log-like relationship between
the call bitrate and user experience in widely used
AMR-WB. The relationship gives rise to a simple and
practical rate control scheme that exponentially
quantizes the steps of rate change, therefore the
name-exponential quantization (EQ). To support that EQ
is effective in addressing the challenge, we show
through a formal analysis that the resulting bandwidth
allocation is optimal in both the overall QoE and the
number of calls served. To relate EQ to existing rate
control mechanisms, we show in a simulation study that
the bitrates of calls administered by EQ converge over
time and outperform those controlled by a (na{\"\i}ve)
greedy mechanism and the mechanism implemented in
Skype.",
acknowledgement = ack-nhfb,
articleno =    "4",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Zhou:2018:OED,
author =       "Ruiting Zhou and Zongpeng Li and Chuan Wu",
title =        "An Online Emergency Demand Response Mechanism for
Cloud Computing",
journal =      j-TOMPECS,
volume =       "3",
number =       "1",
pages =        "5:1--5:??",
month =        feb,
year =         "2018",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3177755",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:15 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3177755",
mechanisms from a data center perspective, where a
cloud participates in a mandatory EDR program while
receiving computing job bids from cloud users in an
online fashion. We target a realistic EDR mechanism
where (i) the cloud provider dynamically packs
different types of resources on servers into requested
VMs and computes job schedules to meet users'
requirements, (ii) the power consumption of servers in
the cloud is limited by the grid through the EDR
program, and (iii) the operation cost of the cloud is
considered in the calculation of social welfare,
measured by an electricity cost that consists of both
volume charge and peak charge. We propose an online
auction for dynamic cloud resource provisioning that is
under the control of the EDR program, runs in
polynomial time, achieves truthfulness, and
close-to-optimal social welfare for the cloud
ecosystem. In the design of the online auction, we
first propose a new framework, compact exponential LPs,
to handle job scheduling constraints in the time
domain. We then develop a posted pricing auction
framework toward the truthful online auction design,
which leverages the classic primal-dual technique for
approximation algorithm design. We evaluate our online
auctions through both theoretical analysis and
empirical studies driven by real-world traces.",
acknowledgement = ack-nhfb,
articleno =    "5",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Koziolek:2018:SIS,
author =       "Anne Koziolek and Evgenia Smirni",
title =        "Special Issue: Selected Paper From the {8th {ACM\slash
SPEC} International Conference on Performance
Engineering (ICPE 2017)}",
journal =      j-TOMPECS,
volume =       "3",
number =       "2",
pages =        "6:1--6:??",
month =        apr,
year =         "2018",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3186329",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:16 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3186329",
acknowledgement = ack-nhfb,
articleno =    "6e",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Wang:2018:EAA,
author =       "Cheng Wang and Qianlin Liang and Bhuvan Urgaonkar",
title =        "An Empirical Analysis of {Amazon EC2} Spot Instance
Features Affecting Cost-Effective Resource
Procurement",
journal =      j-TOMPECS,
volume =       "3",
number =       "2",
pages =        "6:1--6:??",
month =        apr,
year =         "2018",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3164538",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:16 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3164538",
abstract =     "Many cost-conscious public cloud workloads
(tenants'') are turning to Amazon EC2's spot
instances because, on average, these instances offer
significantly lower prices (up to 10 times lower) than
on-demand and reserved instances of comparable
advertised resource capacities. To use spot instances
effectively, a tenant must carefully weigh the lower
costs of these instances against their poorer
availability. Toward this, we empirically study four
features of EC2 spot instance operation that a
cost-conscious tenant may find useful to model. Using
extensive evaluation based on historical spot instance
data, we show shortcomings in the state-of-the-art
modeling of these features that we overcome. As an
extension to our prior work, we conduct data analysis
on a rich dataset of the latest spot price traces
collected from a variety of EC2 spot markets. Our
analysis reveals many novel properties of spot instance
operation, some of which offer predictive value whereas
others do not. Using these insights, we design
predictors for our features that offer a balance
between computational efficiency (allowing for online
resource procurement) and cost efficacy. We explore
case studies'' wherein we implement prototypes of
dynamic spot instance procurement advised by our
predictors for two types of workloads. Compared to the
state of the art, our approach achieves (i) comparable
cost but much better performance (fewer bid failures)
for a latency-sensitive in-memory Memcached cache and
(ii) an additional 18\% cost savings with comparable
(if not better than) performance for a delay-tolerant
acknowledgement = ack-nhfb,
articleno =    "6",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Cassell:2018:DPM,
author =       "Benjamin Cassell and Tyler Szepesi and Jim Summers and
Tim Brecht and Derek Eager and Bernard Wong",
title =        "Disk Prefetching Mechanisms for Increasing {HTTP}
Streaming Video Server Throughput",
journal =      j-TOMPECS,
volume =       "3",
number =       "2",
pages =        "7:1--7:??",
month =        apr,
year =         "2018",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3164536",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:16 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3164536",
abstract =     "Most video streaming traffic is delivered over HTTP
using standard web servers. While traditional web
server workloads consist of requests that are primarily
for small files that can be serviced from the file
system cache, HTTP video streaming workloads often
service a long tail of large infrequently requested
videos. As a result, optimizing disk accesses is
critical to obtaining good server throughput. In this
article we explore serialized, aggressive disk
prefetching, a technique that can be used to improve
the throughput of HTTP streaming video web servers. We
identify how serialization and aggressive prefetching
affect performance, and, based on our findings, we
construct and evaluate Libception, an application-level
shim library that implements both techniques. By
dynamically linking against Libception at runtime,
applications are able to transparently obtain benefits
from serialization and aggressive prefetching without
needing to change their source code. In contrast to
other approaches that modify applications, make kernel
changes, or attempt to optimize kernel tuning,
Libception provides a portable and relatively simple
system in which techniques for optimizing I/O in HTTP
video streaming servers can be implemented and
evaluated. We empirically evaluate the efficacy of
serialization and aggressive prefetching both with and
without Libception, using three web servers (Apache,
nginx, and the userver) running on two operating
systems (FreeBSD and Linux). We find that, by using
Libception, we can improve streaming throughput for all
three web servers by at least a factor of 2 on FreeBSD
and a factor of 2.5 on Linux. Additionally, we find
that with significant tuning of Linux kernel
parameters, we can achieve similar performance to
Libception by globally modifying Linux's disk prefetch
behaviour. Finally, we demonstrate Libception's ability
to reduce the completion time of a microbenchmark
involving two applications competing for disk
resources.",
acknowledgement = ack-nhfb,
articleno =    "7",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Ilyushkin:2018:EPE,
author =       "Alexey Ilyushkin and Ahmed Ali-Eldin and Nikolas
Herbst and Andr{\'e} Bauer and Alessandro V.
Papadopoulos and Dick Epema and Alexandru Iosup",
title =        "An Experimental Performance Evaluation of Autoscalers
for Complex Workflows",
journal =      j-TOMPECS,
volume =       "3",
number =       "2",
pages =        "8:1--8:??",
month =        apr,
year =         "2018",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3164537",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:16 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3164537",
abstract =     "Elasticity is one of the main features of cloud
computing allowing customers to scale their resources
based on the workload. Many autoscalers have been
proposed in the past decade to decide on behalf of
cloud customers when and how to provision resources to
a cloud application based on the workload utilizing
cloud elasticity features. However, in prior work, when
a new policy is proposed, it is seldom compared to the
state-of-the-art, and is often compared only to static
provisioning using a predefined quality of service
target. This reduces the ability of cloud customers and
of cloud operators to choose and deploy an autoscaling
policy, as there is seldom enough analysis on the
performance of the autoscalers in different operating
conditions and with different applications. In our
work, we conduct an experimental performance evaluation
of autoscaling policies, using as application model
workflows, a popular formalism for automating resource
management for applications with well-defined yet
complex structures. We present a detailed comparative
study of general state-of-the-art autoscaling policies,
along with two new workflow-specific policies. To
understand the performance differences between the
seven policies, we conduct various experiments and
compare their performance in both pairwise and group
comparisons. We report both individual and aggregated
metrics. As many workflows have deadline requirements
on the tasks, we study the effect of autoscaling on
effect of autoscaling on the accounted and hourly based
charged costs, and we evaluate performance variability
caused by the autoscaler selection for each group of
workflow sizes. Our results highlight the trade-offs
between the suggested policies, how they can impact
meeting the deadlines, and how they perform in
different operating conditions, thus enabling a better
understanding of the current state-of-the-art.",
acknowledgement = ack-nhfb,
articleno =    "8",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Khan:2018:RAE,
author =       "Kashif Nizam Khan and Mikael Hirki and Tapio Niemi and
Jukka K. Nurminen and Zhonghong Ou",
title =        "{RAPL} in Action: Experiences in Using {RAPL} for
Power Measurements",
journal =      j-TOMPECS,
volume =       "3",
number =       "2",
pages =        "9:1--9:??",
month =        apr,
year =         "2018",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3177754",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:16 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3177754",
abstract =     "To improve energy efficiency and comply with the power
budgets, it is important to be able to measure the
power consumption of cloud computing servers. Intel's
Running Average Power Limit (RAPL) interface is a
powerful tool for this purpose. RAPL provides power
limiting features and accurate energy readings for CPUs
and DRAM, which are easily accessible through different
interfaces on large distributed computing systems.
Since its introduction, RAPL has been used extensively
in power measurement and modeling. However, the
investigated yet. To fill this gap, we conduct a series
of experiments to disclose the underlying strengths and
weaknesses of the RAPL interface by using both
customized microbenchmarks and three well-known
application level benchmarks: Stream, Stress-ng, and
ParFullCMS. Moreover, to make the analysis as realistic
as possible, we leverage two production-level power
measurement datasets from the Taito, a supercomputing
cluster of the Finnish Center of Scientific Computing
and also replicate our experiments on Amazon EC2. Our
results illustrate different aspects of RAPL and
document the findings through comprehensive analysis.
Our observations reveal that RAPL readings are highly
correlated with plug power, promisingly accurate
enough, and have negligible performance overhead.
Experimental results suggest RAPL can be a very useful
tool to measure and monitor the energy consumption of
servers without deploying any complex power meters. We
also show that there are still some open issues, such
as driver support, non-atomicity of register updates,
and unpredictable timings that might weaken the
usability of RAPL in certain scenarios. For such
scenarios, we pinpoint solutions and workarounds.",
acknowledgement = ack-nhfb,
articleno =    "9",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Du:2018:EOL,
author =       "Yuhuan Du and Gustavo {De Veciana}",
title =        "Efficiency and Optimality of Largest Deficit First
Prioritization: Dynamic User Prioritization for Soft
Real-Time Applications",
journal =      j-TOMPECS,
volume =       "3",
number =       "3",
pages =        "10:1--10:??",
month =        aug,
year =         "2018",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3200479",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:16 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3200479",
abstract =     "An increasing number of real-time applications with
compute and/or communication deadlines are being
supported on a shared infrastructure. Such applications
can often tolerate occasional deadline violations
without substantially impacting their Quality of
allocation of shared resources to satisfy such QoS
requirements. We study a simple framework which
decouples this problem into two subproblems: (1)
dynamic prioritization of users based on arbitrary
functions of their deficits (difference of achieved vs.
required QoS), and (2) priority-based resource
allocation on the underlying compute fabric. In this
article, we shall assume the solution to the latter is
fixed, e.g., as realized in the task prioritization
capabilities of current hardware/software, and focus on
compatible solutions to the former. We first
characterize the set of feasible QoS requirements and
show the optimality of max weight-like prioritization.
We then consider simple weighted Largest Deficit First
(w-LDF) prioritization policies, where users with
higher weighted QoS deficits are given higher priority.
The article gives an inner bound for the feasible set
under w-LDF policies, and, under an additional
monotonicity assumption, characterizes its geometry
leading to a sufficient condition for optimality.
Additional insights on the efficiency ratio of w-LDF
policies, the optimality of hierarchical-LDF, and
characterization of clustering of failures are also
discussed.",
acknowledgement = ack-nhfb,
articleno =    "10",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Chen:2018:CEO,
author =       "Shutong Chen and Zhi Zhou and Fangming Liu and
Zongpeng Li and Shaolei Ren",
title =        "{CloudHeat}: An Efficient Online Market Mechanism for
Datacenter Heat Harvesting",
journal =      j-TOMPECS,
volume =       "3",
number =       "3",
pages =        "11:1--11:??",
month =        aug,
year =         "2018",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3199675",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:16 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3199675",
abstract =     "Datacenters are major energy consumers and dissipate
an enormous amount of waste heat. Simple outdoor
discharging of datacenter heat is energy-consuming and
environmentally unfriendly. By harvesting datacenter
waste heat and selling to the district heating system
(DHS), both energy cost compensation and environment
protection can be achieved. To realize such benefits in
practice, an efficient market mechanism is required to
incentivize the participation of datacenters. This work
proposes CloudHeat, an online reverse auction mechanism
for the DHS to solicit heat bids from datacenters. To
minimize long-term social operational cost of the DHS
and the datacenters, we apply a RFHC approach for
decomposing the long-term problem into a series of
one-round auctions, guaranteeing a small loss in
competitive ratio. The one-round optimization is still
NP-hard, and we employ a randomized auction framework
to simultaneously guarantee truthfulness, polynomial
running time, and an approximation ratio of 2. The
performance of CloudHeat is validated through
theoretical analysis and trace-driven simulation
studies.",
acknowledgement = ack-nhfb,
articleno =    "11",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Varki:2018:GGP,
author =       "Elizabeth Varki",
title =        "{GPSonflow}: Geographic Positioning of Storage for
Optimal Nice Flow",
journal =      j-TOMPECS,
volume =       "3",
number =       "3",
pages =        "12:1--12:??",
month =        aug,
year =         "2018",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3197656",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:16 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3197656",
sender to a receiver via the internet when all
transmissions are scheduled for early morning hours.
The significance of early morning hours is that
internet congestion is low while users sleep. When the
sender and receiver lie in proximal time zones, a
direct transmission from sender to receiver can be
scheduled for early morning hours. When the sender and
receiver are separated by several time zones such that
their sleep times are non-overlapping, data can still
be transmitted during early morning hours with an
indirect store-and-forward transfer. The data are
transmitted from the sender to intermediate end
networks or data centers that serve as storage hops en
route to receiver. The storage hops are placed in zones
that are time proximal to the sender or the receiver so
that all transmissions to and from storage hops occur
finds the optimal locations and bandwidth distributions
of storage hops for maximum nice internet flow from a
acknowledgement = ack-nhfb,
articleno =    "12",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Ray:2018:SSC,
author =       "Avik Ray and Sujay Sanghavi and Sanjay Shakkottai",
title =        "Searching for a Single Community in a Graph",
journal =      j-TOMPECS,
volume =       "3",
number =       "3",
pages =        "13:1--13:??",
month =        aug,
year =         "2018",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3200863",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:16 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3200863",
abstract =     "In standard graph clustering/community detection, one
is interested in partitioning the graph into more
densely connected subsets of nodes. In contrast, the
nodes in a single such community, the target, out of
the many communities that may exist. To do so, we are
given suitable side information about the target; for
example, a very small number of nodes from the target
are labeled as such. We consider a general yet simple
notion of side information: all nodes are assumed to
have random weights, with nodes in the target having
higher weights on average. Given these weights and the
graph, we develop a variant of the method of moments
that identifies nodes in the target more reliably, and
with lower computation, than generic community
detection methods that do not use side information and
partition the entire graph. Our empirical results show
significant gains in runtime, and also gains in
accuracy over other graph clustering algorithms.",
acknowledgement = ack-nhfb,
articleno =    "13",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Malik:2018:SAL,
author =       "Maria Malik and Setareh Rafatirad and Houman
Homayoun",
title =        "System and Architecture Level Characterization of Big
Data Applications on Big and Little Core Server
Architectures",
journal =      j-TOMPECS,
volume =       "3",
number =       "3",
pages =        "14:1--14:??",
month =        aug,
year =         "2018",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3229049",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:16 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3229049",
abstract =     "The rapid growth in data yields challenges to process
data efficiently using current high-performance server
architectures such as big Xeon cores. Furthermore,
physical design constraints, such as power and density,
have become the dominant limiting factor for scaling
out servers. Low-power embedded cores in servers such
as little Atom have emerged as a promising solution to
enhance energy-efficiency to address these challenges.
Therefore, the question of whether to process the big
data applications on big Xeon- or Little Atom-based
servers becomes important. In this work, through
methodical investigation of power and performance
measurements, and comprehensive application-level,
system-level, and micro-architectural level analysis,
we characterize dominant big data applications on big
Xeon- and little Atom-based server architectures. The
characterization results across a wide range of
real-world big data applications, and various software
stacks demonstrate how the choice of big- versus
little-core-based server for energy-efficiency is
significantly influenced by the size of data,
performance constraints, and presence of accelerator.
In addition, we analyze processor resource utilization
of this important class of applications,such as memory
footprints, CPU utilization, and disk bandwidth, to
understand their run-time behavior. Furthermore, we
perform micro-architecture-level analysis to highlight
where improvement is needed in big- and little-core
bottlenecks.",
acknowledgement = ack-nhfb,
articleno =    "14",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Li:2018:MFG,
author =       "Jian Li and Bainan Xia and Xinbo Geng and Hao Ming and
Srinivas Shakkottai and Vijay Subramanian and Le Xie",
title =        "Mean Field Games in Nudge Systems for Societal
Networks",
journal =      j-TOMPECS,
volume =       "3",
number =       "4",
pages =        "15:1--15:??",
month =        sep,
year =         "2018",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3232076",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:16 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3232076",
abstract =     "We consider the general problem of resource sharing in
societal networks, consisting of interconnected
communication, transportation, energy, and other
networks important to the functioning of society.
Participants in such network need to take decisions
daily, both on the quantity of resources to use as well
as the periods of usage. With this in mind, we discuss
the problem of incentivizing users to behave in such a
way that society as a whole benefits. To perceive
societal level impact, such incentives may take the
form of rewarding users with lottery tickets based on
good behavior and periodically conducting a lottery to
translate these tickets into real rewards. We will pose
the user decision problem as a mean field game and the
incentives question as one of trying to select a good
mean field equilibrium (MFE). In such a framework, each
agent (a participant in the societal network) takes a
decision based on an assumed distribution of actions of
his/her competitors and the incentives provided by the
social planner. The system is said to be at MFE if the
agent's action is a sample drawn from the assumed
distribution. We will show the existence of such an MFE
under general settings, and also illustrate how to
choose an attractive equilibrium using as an example
demand-response in the (smart) electricity network.",
acknowledgement = ack-nhfb,
articleno =    "15",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Pajevic:2018:EPC,
author =       "Ljubica Pajevic and Viktoria Fodor and Gunnar
Karlsson",
title =        "Ensuring Persistent Content in Opportunistic Networks
via Stochastic Stability Analysis",
journal =      j-TOMPECS,
volume =       "3",
number =       "4",
pages =        "16:1--16:??",
month =        sep,
year =         "2018",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3232161",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:16 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3232161",
abstract =     "The emerging device-to-device communication solutions
and the abundance of mobile applications and services
make opportunistic networking not only a feasible
solution but also an important component of future
wireless networks. Specifically, the distribution of
locally relevant content could be based on the
community of mobile users visiting an area, if
long-term content survival can be ensured this way. In
survival in such opportunistic networks, considering
the user mobility patterns, as well as the time users
keep forwarding the content, as the controllable system
parameter. We model the content spreading with an
epidemic process, and derive a stochastic differential
equations based approximation. By means of stability
analysis, we determine the necessary user contribution
to ensure content survival. We show that the required
contribution from the users depends significantly on
the size of the population, that users need to
redistribute content only in a short period within
their stay, and that they can decrease their
contribution significantly in crowded areas. Hence,
with the appropriate control of the system parameters,
opportunistic content sharing can be both reliable and
sustainable.",
acknowledgement = ack-nhfb,
articleno =    "16",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Wang:2018:QMS,
author =       "Weikun Wang and Giuliano Casale and Ajay Kattepur and
Manoj K. Nambiar",
title =        "{QMLE}: A Methodology for Statistical Inference of
Service Demands from Queueing Data",
journal =      j-TOMPECS,
volume =       "3",
number =       "4",
pages =        "17:1--17:??",
month =        sep,
year =         "2018",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3233180",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:16 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3233180",
abstract =     "Estimating the demands placed by services on physical
resources is an essential step for the definition of
performance models. For example, scalability analysis
relies on these parameters to predict queueing delays
maximum likelihood (ML) estimators for demands at
systems with parallelism constraints. We define a
likelihood function based on state measurements and
derive necessary conditions for its maximization. We
then obtain novel estimators that accurately and
inexpensively obtain service demands using only
aggregate state data. With our approach, and also
thanks to approximation methods for computing marginal
and joint distributions for the load-dependent case,
confidence intervals can be rigorously derived,
explicitly taking into account both topology and
concurrency levels of the services. Our estimators and
their confidence intervals are validated against
simulations and real system measurements for two
multi-tier applications, showing high accuracy also in
acknowledgement = ack-nhfb,
articleno =    "17",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Azimi:2018:SVS,
author =       "Reza Azimi and Tyler Fox and Wendy Gonzalez and
Sherief Reda",
title =        "Scale-Out vs Scale-Up: A Study of {ARM}-based {SoCs}
journal =      j-TOMPECS,
volume =       "3",
number =       "4",
pages =        "18:1--18:??",
month =        sep,
year =         "2018",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3232162",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:16 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/pvm.bib;
http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3232162",
abstract =     "ARM 64-bit processing has generated enthusiasm to
develop ARM-based servers that are targeted for both
data centers and supercomputers. In addition to the
server-class components and hardware advancements, the
ARM software environment has grown substantially over
the past decade. Major development ecosystems and
libraries have been ported and optimized to run on ARM,
making ARM suitable for server-class workloads. There
are two trends in available ARM SoCs: mobile-class ARM
SoCs that rely on the heterogeneous integration of a
mix of CPU cores, GPGPU streaming multiprocessors
(SMs), and other accelerators, and the server-class
SoCs that instead rely on integrating a larger number
of CPU cores with no GPGPU support and a number of IO
accelerators. For scaling the number of processing
cores, there are two different paradigms: mobile-class
SoCs that use scale-out architecture in the form of a
cluster of simpler systems connected over a network,
and server-class ARM SoCs that use the scale-up
solution and leverage symmetric multiprocessing to pack
we present ScaleSoC cluster, which is a scale-out
solution based on mobile class ARM SoCs. ScaleSoC
leverages fast network connectivity and GPGPU
acceleration to improve performance and energy
efficiency compared to previous ARM scale-out clusters.
We consider a wide range of modern server-class
MPI-based CPU and GPGPU-accelerated scientific
applications, and emerging artificial intelligence
workloads. We study the performance and energy
efficiency of ScaleSoC compared to server-class ARM
SoCs and discrete GPGPUs in depth. We quantify the
network overhead on the performance of ScaleSoC and
show that packing a large number of ARM cores on a
single chip does not necessarily guarantee better
performance, due to the fact that shared resources,
such as last-level cache, become performance
bottlenecks. We characterize the GPGPU accelerated
workloads and demonstrate that for applications that
can leverage the better CPU-GPGPU balance of the
ScaleSoC cluster, performance and energy efficiency
improve compared to discrete GPGPUs.",
acknowledgement = ack-nhfb,
articleno =    "18",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Herbst:2018:QCP,
author =       "Nikolas Herbst and Andr{\'e} Bauer and Samuel Kounev
and Giorgos Oikonomou and Erwin {Van Eyk} and George
Kousiouris and Athanasia Evangelinou and Rouven Krebs
and Tim Brecht and Cristina L. Abad and Alexandru
Iosup",
title =        "Quantifying Cloud Performance and Dependability:
Taxonomy, Metric Design, and Emerging Challenges",
journal =      j-TOMPECS,
volume =       "3",
number =       "4",
pages =        "19:1--19:??",
month =        sep,
year =         "2018",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3236332",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:16 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3236332",
abstract =     "In only a decade, cloud computing has emerged from a
pursuit for a service-driven information and
communication technology (ICT), becoming a significant
fraction of the ICT market. Responding to the growth of
the market, many alternative cloud services and their
underlying systems are currently vying for the
attention of cloud users and providers. To make
informed choices between competing cloud service
providers, permit the cost-benefit analysis of
cloud-based systems, and enable system DevOps to
evaluate and tune the performance of these complex
ecosystems, appropriate performance metrics,
benchmarks, tools, and methodologies are necessary.
This requires re-examining old system properties and
considering new system properties, possibly leading to
the re-design of classic benchmarking metrics such as
expressing performance as throughput and latency
(response time). In this work, we address these
requirements by focusing on four system properties: (i)
elasticity of the cloud service, to accommodate large
variations in the amount of service requested, (ii){\~
}performance isolation between the tenants of shared
cloud systems and resulting performance variability,
(iii){\~ }availability of cloud services and systems,
and (iv) the operational risk of running a production
system in a cloud environment. Focusing on key metrics
for each of these properties, we review the
state-of-the-art, then select or propose new metrics
together with measurement approaches. We see the
presented metrics as a foundation toward upcoming,
future industry-standard cloud benchmarks.",
acknowledgement = ack-nhfb,
articleno =    "19",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Jiang:2018:CTA,
author =       "Bo Jiang and Philippe Nain and Don Towsley",
title =        "On the Convergence of the {TTL} Approximation for an
{LRU} Cache under Independent Stationary Request
Processes",
journal =      j-TOMPECS,
volume =       "3",
number =       "4",
pages =        "20:1--20:??",
month =        sep,
year =         "2018",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3239164",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:16 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3239164",
abstract =     "The modeling and analysis of an LRU cache is extremely
challenging as exact results for the main performance
metrics (e.g., hit rate) are either lacking or cannot
be used because of their high computational complexity
for large caches. As a result, various approximations
have been proposed. The state-of-the-art method is the
so-called TTL approximation, first proposed and shown
to be asymptotically exact for IRM requests by Fagin
[13]. It has been applied to various other workload
models and numerically demonstrated to be accurate but
provide theoretical justification for the approximation
in the case where distinct contents are described by
independent stationary and ergodic processes. We show
that this approximation is exact as the cache size and
the number of contents go to infinity. This extends
earlier results for the independent reference model.
Moreover, we establish results not only for the
aggregate cache hit probability but also for every
individual content. Last, we obtain bounds on the rate
of convergence.",
acknowledgement = ack-nhfb,
articleno =    "20",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Abid:2018:LR,
author =       "Amine Abid",
title =        "List of Reviewers",
journal =      j-TOMPECS,
volume =       "3",
number =       "4",
pages =        "21:1--21:??",
month =        sep,
year =         "2018",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3271430",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:16 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3271430",
acknowledgement = ack-nhfb,
articleno =    "21",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Yin:2019:ETL,
author =       "Ping Yin and Sen Yang and Jun Xu and Jim Dai and Bill
Lin",
title =        "Efficient Traffic Load-Balancing via Incremental
Expansion of Routing Choices",
journal =      j-TOMPECS,
volume =       "4",
number =       "1",
pages =        "1:1--1:??",
month =        mar,
year =         "2019",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3243173",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:17 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3243173",
abstract =     "Routing policies play a major role in the performance
routing algorithms where traffic is load balanced along
different routing paths on a per-packet basis have been
studied extensively in the literature. Although
backpressure-based algorithms have been shown to be
networkwide throughput optimal, they typically have
poor delay performance under light or moderate loads,
because packets may be sent over unnecessarily long
routes. Further, backpressure-based algorithms have
required every node to compute differential backlogs
for every per-destination queue with the corresponding
per-destination queue at every adjacent node, which is
expensive given the large number of possible pairwise
differential backlogs between neighbor nodes. In this
article, we propose new backpressure-based adaptive
routing algorithms that only use shortest-path routes
to destinations when they are sufficient to accommodate
the given traffic load, but the proposed algorithms
will incrementally expand routing choices as needed to
accommodate increasing traffic loads. We show
analytically by means of fluid analysis that the
proposed algorithms retain networkwide throughput
optimality, and we show empirically by means of
simulations that our proposed algorithms provide
substantial improvements in delay performance. Our
evaluations further show that, in practice, our
approach dramatically reduces the number of pairwise
differential backlogs that have to be computed and the
be exchanged, because routing choices are only
incrementally expanded as needed.",
acknowledgement = ack-nhfb,
articleno =    "1",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Chen:2019:EQA,
author =       "Hao Chen and Yijia Zhang and Michael C. Caramanis and
Ayse K. Coskun",
title =        "{EnergyQARE}: {QoS}-Aware Data Center Participation in
Smart Grid Regulation Service Reserve Provision",
journal =      j-TOMPECS,
volume =       "4",
number =       "1",
pages =        "2:1--2:??",
month =        mar,
year =         "2019",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3243172",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:17 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3243172",
abstract =     "Power market operators have recently introduced smart
grid demand response (DR), in which electricity
consumers regulate their power usage following market
requirements. DR helps stabilize the grid and enables
integrating a larger amount of intermittent renewable
power generation. Data centers provide unique
opportunities for DR participation due to their
flexibility in both workload servicing and power
consumption. While prior studies have focused on data
center participation in legacy DR programs such as
studies data centers in emerging DR programs, i.e.,
demand side capacity reserves. Among different types of
capacity reserves, regulation service reserves (RSRs)
are especially attractive due to their relatively
Energy and Quality-of-Service (QoS) Aware R SR Enabler,
an approach that enables data center RSR provision in
real-life scenarios. EnergyQARE not only provides a
bidding strategy in RSR provision, but also contains a
runtime policy that adaptively modulates data center
power through server power management and server
provisioning based on workload QoS feedback. To reflect
real-life scenarios, this runtime policy handles a
heterogeneous set of jobs and considers transition time
delay of servers. Simulated numerical results
demonstrate that in a general data center scenario,
EnergyQARE provides close to 50\% of data center
average power consumption as reserves to the market and
saves up to 44\% in data center electricity cost, while
still meeting workload QoS constraints. Case studies in
not sensitive to a specific type of non-interactive
workload, or the size of the data center, although they
depend strongly on data center utilization and
parameters of server power states.",
acknowledgement = ack-nhfb,
articleno =    "2",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Li:2019:QCS,
author =       "Zhenhua Li and Yongfeng Zhang and Yunhao Liu and
Tianyin Xu and Ennan Zhai and Yao Liu and Xiaobo Ma and
Zhenyu Li",
title =        "A Quantitative and Comparative Study of Network-Level
Efficiency for Cloud Storage Services",
journal =      j-TOMPECS,
volume =       "4",
number =       "1",
pages =        "3:1--3:??",
month =        mar,
year =         "2019",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3274526",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:17 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3274526",
abstract =     "Cloud storage services such as Dropbox and OneDrive
provide users with a convenient and reliable way to
store and share data from anywhere, on any device, and
at any time. Their cornerstone is the data
synchronization (sync) operation, which automatically
maps the changes in users' local file systems to the
cloud via a series of network communications in a
timely manner. Without careful design and
implementation, however, the data sync mechanisms could
generate overwhelming traffic, causing tremendous
financial overhead and performance penalties to both
a simple yet critical question: Is the current data
sync traffic of cloud storage services efficiently
used? We first define a novel metric TUE to quantify
the Traffic Usage Efficiency of data
synchronization. Then, by conducting comprehensive
benchmark experiments and reverse engineering the data
sync processes of eight widely used cloud storage
services, we uncover their manifold practical endeavors
for optimizing the TUE, including three intra-file
approaches (compression, incremental sync, and
interrupted transfer resumption), two cross-file/-user
approaches ( i.e., deduplication and peer-assisted
sync deferment), and two web-specific approaches
measurement results reveal that a considerable portion
of the data sync traffic is, in a sense, wasteful and
can be effectively avoided or significantly reduced via
carefully designed data sync mechanisms. Most
importantly, our study not only offers practical,
actionable guidance for providers to build more
efficient, traffic-economic services, but also helps
end users pick appropriate services that best fit their
use cases and budgets.",
acknowledgement = ack-nhfb,
articleno =    "3",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Gupta:2019:ERS,
author =       "Samarth Gupta and Sharayu Moharir",
title =        "Effect of Recommendations on Serving Content with
Unknown Demand",
journal =      j-TOMPECS,
volume =       "4",
number =       "1",
pages =        "4:1--4:??",
month =        mar,
year =         "2019",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3289324",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:17 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3289324",
abstract =     "We consider the task of content replication in
distributed content delivery systems used by
Video-on-Demand (VoD) services with large content
catalogs. The prior work in this area focuses on the
setting where each request is generated independent of
all past requests. Motivated by the fact that most
popular VoD services offer recommendations to users
based on their viewing history, in a departure from
existing studies, we study the setting with
time-correlation in requests coming from each user. We
use a Markovian process to model each user's request
process. In addition to introducing time-correlation in
user requests, our model is consistent with empirically
observed properties of the request process for VoD
services with recommendation engines. In the setting
where the underlying Markov Chain is unknown and has to
be learned from the very requests the system is trying
to serve, we show that separating the task of
estimating content popularity and using the estimates
to design a static content replication policy is
strictly sub-optimal. To prove this, we show that an
estimation and content replication, outperforms all
policies that separate the task of estimation and
content replication.",
acknowledgement = ack-nhfb,
articleno =    "4",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Harrison:2019:MRT,
author =       "P. G. Harrison and N. M. Patel and J. F. P{\'e}rez and
Z. Qiu",
title =        "Managing Response Time Tails by Sharding",
journal =      j-TOMPECS,
volume =       "4",
number =       "1",
pages =        "5:1--5:??",
month =        mar,
year =         "2019",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3300143",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:17 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3300143",
abstract =     "Matrix analytic methods are developed to compute the
probability distribution of response times (i.e., data
access times) in distributed storage systems protected
by erasure coding, which is implemented by sharding a
data object into N fragments, only K \N of which are
required to reconstruct the object. This leads to a
partial-fork-join model with a choice of canceling
policies for the redundant N - K tasks. The accuracy of
the analytical model is supported by tests against
simulation in a broad range of setups. At increasing
workload intensities, numerical results show the extent
to which increasing the redundancy level reduces the
mean response time of storage reads and significantly
flattens the tail of their distribution; this is
demonstrated at medium-high quantiles, up to the 99th.
The quantitative reduction in response time achieved by
two policies for canceling redundant tasks is also
shown: for cancel-at-finish and cancel-at-start, which
benefit of selectivity amongst fragment service
times.",
acknowledgement = ack-nhfb,
articleno =    "5",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{KhudaBukhsh:2019:PPE,
author =       "Wasiur R. KhudaBukhsh and Sounak Kar and Amr Rizk and
Heinz Koeppl",
title =        "Provisioning and Performance Evaluation of Parallel
Systems with Output Synchronization",
journal =      j-TOMPECS,
volume =       "4",
number =       "1",
pages =        "6:1--6:??",
month =        mar,
year =         "2019",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3300142",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:17 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3300142",
abstract =     "Parallel server frameworks are widely deployed in
modern large-data processing applications. Intuitively,
splitting and parallel processing of the workload
provides accelerated application response times and
scaling flexibility. Examples of such frameworks
include MapReduce, Hadoop, and Spark. For many
applications, the dynamics of such systems are
naturally captured by a Fork-Join (FJ) queuing model,
where incoming jobs are split into tasks each of which
is mapped to exactly one server. When all the tasks
that belong to one job are executed, the job is
reassembled and leaves the system. We consider this
behavior at the output as a synchronization constraint.
parallel systems for different server properties, e.g.,
work-conservingness, phase-type behavior, and as
suggested by recent evidence, for bursty input job
arrivals. We establish a Large Deviations Principle for
the steady-state job waiting times in an FJ system
based on Markov-additive processes. Building on that,
we present a performance analysis framework for FJ
systems and provide computable bounds on the tail
probabilities of the steady-state waiting times. We
validate our bounds using estimates obtained through
simulations. In addition, we define and analyze
provisioning, a flexible division of jobs into tasks,
in FJ systems. Finally, we use this framework together
with real-world traces to show the benefits of an
within an FJ system based on the arrival intensity.",
acknowledgement = ack-nhfb,
articleno =    "6",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Wang:2019:ESR,
author =       "Da Wang and Gauri Joshi and Gregory W. Wornell",
title =        "Efficient Straggler Replication in Large-Scale
Parallel Computing",
journal =      j-TOMPECS,
volume =       "4",
number =       "2",
pages =        "7:1--7:??",
month =        jun,
year =         "2019",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3310336",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:17 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3310336",
abstract =     "In a cloud computing job with many parallel tasks, the
the bottleneck in the job completion. Computing
frameworks such as MapReduce and Spark tackle this by
replicating the straggling tasks and waiting for any
one copy to finish. Despite being adopted in practice,
there is little analysis of how replication affects the
latency and the cost of additional computing resources.
latency-cost tradeoff and find the best replication
strategy by answering design questions, such as (1)
when to replicate straggling tasks, (2) how many
replicas to launch, and (3) whether to kill the
original copy or not. Our analysis reveals that for
certain execution time distributions, a small amount of
task replication can drastically reduce both latency
and the cost of computing resources. We also propose an
algorithm to estimate the latency and cost based on the
empirical distribution of task execution time.
Evaluations using samples in the Google Cluster Trace
suggest further latency and cost reduction compared to
the existing replication strategy used in MapReduce.",
acknowledgement = ack-nhfb,
articleno =    "7",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

author =       "Ramin Izadpanah and Benjamin A. Allan and Damian
Dechev and Jim Brandt",
title =        "Production Application Performance Data Streaming for
System Monitoring",
journal =      j-TOMPECS,
volume =       "4",
number =       "2",
pages =        "8:1--8:??",
month =        jun,
year =         "2019",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3319498",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:17 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/pvm.bib;
http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3319498",
collection of application performance data. Practical
application performance tuning and troubleshooting in
production high-performance computing (HPC)
environments requires an understanding of how
applications interact with the platform, including (but
not limited to) parallel programming libraries such as
Message Passing Interface (MPI). Several profiling and
tracing tools exist that collect heavy runtime data
traces either in memory (released only at application
exit) or on a file system (imposing an I/O load that
may interfere with the performance being measured).
Although these approaches are beneficial in development
stages and post-run analysis, a systemwide and
low-overhead method is required to monitor deployed
applications continuously. This method must be able to
collect information at both the application and system
levels to yield a complete performance picture. In our
approach, an application profiler collects application
event counters. A sampler uses an efficient
inter-process communication method to periodically
extract the application counters and stream them into
an infrastructure for performance data collection. We
implement a tool-set based on our approach and
integrate it with the Lightweight Distributed Metric
Service (LDMS) system, a monitoring system used on
large-scale computational platforms. LDMS provides the
infrastructure to create and gather streams of
performance data in a low overhead manner. We
demonstrate our approach using applications implemented
with MPI, as it is one of the most common standards for
the development of large-scale scientific applications.
We utilize our tool-set to study the impact of our
approach on an open source HPC application, Nalu. Our
tool-set enables us to efficiently identify patterns in
the behavior of the application without source-level
knowledge. We leverage LDMS to collect system-level
performance data and explore the correlation between
the system and application events. Also, we demonstrate
how our tool-set can help detect anomalies with a low
latency. We run tests on two different architectures: a
system enabled with Intel Xeon Phi and another system
equipped with Intel Xeon processor. Our overhead study
shows our method imposes at most 0.5\% CPU usage
overhead on the application in realistic deployment
scenarios.",
acknowledgement = ack-nhfb,
articleno =    "8",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Hellemans:2019:PRD,
author =       "Tim Hellemans and Benny {Van Houdt}",
title =        "Performance of Redundancy($d$) with Identical\slash
Independent Replicas",
journal =      j-TOMPECS,
volume =       "4",
number =       "2",
pages =        "9:1--9:??",
month =        jun,
year =         "2019",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3316768",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:17 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3316768",
abstract =     "Queueing systems with redundancy have received
considerable attention recently. The idea of redundancy
is to reduce latency by replicating each incoming job a
number of times and to assign these replicas to a set
of randomly selected servers. As soon as one replica
completes service the remaining replicas are cancelled.
Most prior work on queueing systems with redundancy
assumes that the job durations of the different
replicas are independent and identically distributed
(i.i.d.), which yields insights that can be misleading
a differential equation, using the cavity method, to
assess the workload and response time distribution in a
large homogeneous system with redundancy without the
need to rely on this independence assumption. More
specifically, we assume that the duration of each
replica of a single job is identical across the servers
and follows a general service time distribution.
Simulation results suggest that the differential
equation yields exact results as the system size tends
to infinity and can be used to study the stability of
the system. We also compare our system to the one with
i.i.d. replicas and show the similarity in the analysis
used for independent, respectively, identical
replicas.",
acknowledgement = ack-nhfb,
articleno =    "9",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Wang:2019:OTA,
author =       "Qingyang Wang and Shungeng Zhang and Yasuhiko Kanemasa
and Calton Pu and Balaji Palanisamy and Lilian Harada
and Motoyuki Kawaba",
title =        "Optimizing {$N$}-Tier Application Scalability in the
Cloud: A Study of Soft Resource Allocation",
journal =      j-TOMPECS,
volume =       "4",
number =       "2",
pages =        "10:1--10:??",
month =        jun,
year =         "2019",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3326120",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:17 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/java2010.bib;
http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3326120",
abstract =     "An effective cloud computing environment requires both
good performance and high efficiency of computing
resources. Through extensive experiments using a
representative n-tier benchmark application (Rice
University Bulletin Board System (RUBBoS)), we show
that the soft resource allocation (e.g., thread pool
size and database connection pool size) in component
servers has a significant impact on the overall system
performance, especially at high system utilization
scenarios. Concretely, the same software resource
allocation can be a good setting in one hardware
configuration and then becomes an either under- or
over-allocation in a slightly different hardware
configuration, causing a significant performance drop.
We have also observed some interesting phenomena that
were caused by the non-trivial dependencies between the
soft resources of servers in different tiers. For
instance, the thread pool size in an Apache web server
can limit the total number of concurrent requests to
the downstream servers, which surprisingly decreases
the Central Processing Unit (CPU) utilization of the
Clustered Java Database Connectivity (C-JDBC)
clustering middleware as the workload increases. To
provide a globally optimal (or near-optimal) soft
resource allocation of each tier in the system, we
propose a practical iterative solution approach by
combining a soft resource aware queuing network model
and the fine-grained measurement data of every
component server. Our results show that to truly scale
complex distributed systems such as n-tier web
applications with expected performance in the cloud, we
need to carefully manage soft resource allocation in
the system.",
acknowledgement = ack-nhfb,
articleno =    "10",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Namazi:2019:SSO,
author =       "Alireza Namazi and Saeed Safari and Siamak Mohammadi
and Meisam Abdollahi",
title =        "{SORT}: Semi Online Reliable Task Mapping for Embedded
Multi-Core Systems",
journal =      j-TOMPECS,
volume =       "4",
number =       "2",
pages =        "11:1--11:??",
month =        jun,
year =         "2019",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3322899",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:17 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3322899",
(SORT) mapping approach to many-core platforms divided
into two sections: offline and online. The offline
section is a twofolded approach. It maintains the
reliability of the mapped task graph against soft
errors considering the reliability threshold defined by
designers. As wear-out mechanisms decrease the lifetime
of the system, our proposed approach increases the
It specifies task migration plans with the minimum
overhead using a novel heuristic approach. SORT
maintains the required level of reliability of the task
graph in the whole lifetime of the system using a
replication technique with minimum replica overhead,
maximum achievable performance, and minimum temperature
increase. The online segment uses migration plans
obtained in the offline segment to increase the
lifetime and also permanently maintains the reliability
threshold for the task graph during runtime. Results
show that the effectiveness of SORT improves on bigger
mesh sizes and higher reliability thresholds.
Simulation results obtained from real benchmarks show
that the proposed approach decreases design-time
calculation up to 4,371\% compared to exhaustive
exploration while achieving a lifetime negligibly lower
than the exhaustive solution (up to 5.83\%).",
acknowledgement = ack-nhfb,
articleno =    "11",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Nguyen:2019:PFR,
author =       "T. T. Hang Nguyen and Olivier Brun and Balakrishna J.
Prabhu",
title =        "Performance of a Fixed Reward Incentive Scheme for
Two-hop {DTNs} with Competing Relays",
journal =      j-TOMPECS,
volume =       "4",
number =       "2",
pages =        "12:1--12:??",
month =        jun,
year =         "2019",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3325288",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:17 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3325288",
abstract =     "We analyze the performance of an incentive scheme for
two-hop Delay-Tolerant Networks (DTNs) in which a
backlogged source proposes a fixed reward to the relays
to deliver a message. Only one message at a time is
proposed by the source. For a given message, only the
first relay to deliver it gets the reward corresponding
to this message thereby inducing a competition between
the relays. The relays seek to maximize the expected
reward for each message, whereas the objective of the
source is to satisfy a given constraint on the
probability of message delivery. We show that the
optimal policy of a relay is of threshold type: it
accepts a message until a first threshold and then
keeps the message until it either meets the destination
or reaches the second threshold. Formulas for computing
the thresholds as well as probability of message
delivery are derived for a backlogged source.",
acknowledgement = ack-nhfb,
articleno =    "12",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Verschoren:2019:EDC,
author =       "Robin Verschoren and Benny {Van Houdt}",
title =        "On the Endurance of the $d$-Choices Garbage Collection
Algorithm for Flash-Based {SSDs}",
journal =      j-TOMPECS,
volume =       "4",
number =       "3",
pages =        "13:1--13:??",
month =        sep,
year =         "2019",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3326121",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:17 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3326121",
abstract =     "Garbage collection (GC) algorithms for flash-based
solid-state drives (SSDs) have a profound impact on its
performance and many studies have focused on assessing
the so-called write amplification of various GC
d -choices GC algorithms and study (a) the extent in
which these algorithms induce unequal wear and (b) the
manner in which they affect the lifetime of the drive.
For this purpose, we introduce two performance
measures: PE fairness and SSD endurance. We study the
impact of the d -choices GC algorithm on both these
measures under different workloads (uniform, synthetic
and trace-based) when combined with two different write
modes. Numerical results show that the more complex of
the two write modes, which requires hot/cold data
identification, may not necessarily give rise to a
significantly better SSD endurance. Further, the d
-choices GC algorithm is often shown to strike a good
balance between garbage collection and wear leveling
for small d values (e.g., d = 10), yielding high
endurance.",
acknowledgement = ack-nhfb,
articleno =    "13",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Phan:2019:NFE,
author =       "Tien-Dat Phan and Guillaume Pallez and Shadi Ibrahim
title =        "A New Framework for Evaluating Straggler Detection
Mechanisms in {MapReduce}",
journal =      j-TOMPECS,
volume =       "4",
number =       "3",
pages =        "14:1--14:??",
month =        sep,
year =         "2019",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3328740",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:17 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3328740",
abstract =     "Big Data systems (e.g., Google MapReduce, Apache
Hadoop, Apache Spark) rely increasingly on speculative
because a job's execution time is dominated by the
slowest task instance. Big Data systems typically
identify stragglers and speculatively run copies of
those tasks with the expectation that a copy may
complete faster to shorten job execution times. There
is a rich body of recent results on straggler
mitigation in MapReduce. However, the majority of these
do not consider the problem of accurately detecting
detection approach and then study its effectiveness in
terms of performance, e.g., reduction in job completion
time or higher efficiency, e.g., high resource
framework for straggler detection and mitigation. We
characterize and detect stragglers including Precision,
Recall, Detection Latency, Undetected Time, and Fake
Positive. We then develop an architectural model by
which these metrics can be linked to measures of
performance including execution time and system energy
overheads. We further conduct a series of experiments
to demonstrate which metrics and approaches are more
effective in detecting stragglers and are also
predictive of effectiveness in terms of performance and
energy efficiencies. For example, our results indicate
that the default Hadoop straggler detector could be
made more effective. In a certain case, Precision is
low and only 55\% of those detected are actual
stragglers and the Recall, i.e., percent of actual
detected stragglers, is also relatively low at 56\%.
For the same case, the hierarchical approach (i.e., a
green-driven detector based on the default one)
achieves a Precision of 99\% and a Recall of 29\%. This
increase in Precision can be translated to achieve
lower execution time and energy consumption, and thus
higher performance and energy efficiency; compared to
the default Hadoop mechanism, the energy consumption is
reduced by almost 31\%. These results demonstrate how
our framework can offer useful insights and be applied
in practical settings to characterize and design new
straggler detection mechanisms for MapReduce systems.",
acknowledgement = ack-nhfb,
articleno =    "14",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Lim:2019:PCI,
author =       "Chiun Lin Lim and Ki Suh Lee and Han Wang and Hakim
Weatherspoon and Ao Tang",
title =        "Packet Clustering Introduced by Routers: Modeling,
Analysis, and Experiments",
journal =      j-TOMPECS,
volume =       "4",
number =       "3",
pages =        "15:1--15:??",
month =        sep,
year =         "2019",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3345032",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:17 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3345032",
variation on packet processing time and its effect on
interpacket delay and packet clustering. We propose a
simple pipeline model incorporating the inherent
variation, and two metrics-one to measure packet
clustering and one to quantify inherent variation. To
isolate the effect of the inherent variation, we begin
our analysis with no cross traffic and step through
setups where the input streams have different data
rates, packet size, and go through a different number
of hops. We show that a homogeneous input stream with a
sufficiently large interpacket gap will emerge at the
router's output with interpacket delays that are
negative correlated with adjacent values and have
symmetrical distributions. We show that for smaller
interpacket gaps, the change in packet clustering is
smaller. It is also shown that the degree of packet
clustering could in fact decrease for a clustered
input. We generalize our results by adding cross
traffic. All the results predicted by the model are
validated with experiments with real routers. We also
investigated several factors that can affect the
inherent variation as well as some potential
applications of this study.",
acknowledgement = ack-nhfb,
articleno =    "15",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Bakhshaliyev:2019:ICI,
author =       "Khalid Bakhshaliyev and Muhammed Abdullah Canbaz and
title =        "Investigating Characteristics of {Internet} Paths",
journal =      j-TOMPECS,
volume =       "4",
number =       "3",
pages =        "16:1--16:??",
month =        sep,
year =         "2019",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3342286",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:17 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3342286",
abstract =     "Interactive and multimedia applications depend on the
stability of end-to-end paths for predictable
performance and good quality of service. On the other
hand, network providers depend on multiple paths to
ensure fault tolerance and use load balancing between
these paths to enhance the overall network throughput.
In this study, we analyze path dynamics for both
end-to-end paths and path segments within service
providers' networks using 2 months of measurement data
from the RIPE Atlas platform, which collects path
traces between a fixed set of source and destination
pairs every 15 minutes. We observe that 78\% of the
end-to-end routes have at least two alternative
Autonomous System (AS) paths with some end-to-end
routes going through hundreds of different AS paths
during the 2 months of analysis. While AS level paths
are often prevalent for a day, there are considerable
changes in the routing of the trace packets over the
ASes for a longer duration of a month or longer.
Analyzing end-to-end paths for routing anomalies, we
observe that 4.4\% of the path traces (involving 18\%
of the ASes) contain routing loops indicating
misconfiguration of routers. Some of the ASes had over
100 routers involved in loops in path traces through
their networks. We observe a much higher rate of
anomalies in the AS level, with 45\% of path traces
containing an AS loop. Additionally, we discovered that
few of the ASes bounce-back packets where some traces
through their network traverse routers in both forward
and backward directions. Determining path segments
belonging to each AS, we further explore ingress to
egress paths of ASes in addition to the source to
destination paths within the AS. Analyzing trace
segments between ingresses and egresses of an AS, we
realized more than half of the ASes have the same
router level path between any ingress-egress pair for
the 2 months, but others implement load balancing.
These results are different from earlier studies that
indicated a high level of path dynamism. Our results
indicate that the end-to-end path dynamism is due to
the Border Gateway Protocol level rather than the
router level within ASes.",
acknowledgement = ack-nhfb,
articleno =    "16",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Skevakis:2019:SOF,
author =       "Emmanouil Skevakis and Ioannis Lambadaris and Hassan
Halabian",
title =        "Scheduling for Optimal File-Transfer Delay using
Chunked Random Linear Network Coding Broadcast",
journal =      j-TOMPECS,
volume =       "4",
number =       "3",
pages =        "17:1--17:??",
month =        sep,
year =         "2019",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3340242",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:17 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3340242",
abstract =     "We study the broadcast transmission of a single file
to an arbitrary number of receivers using Random Linear
Network Coding (RLNC) in a network with unreliable
channels. Due to the increased computational complexity
of the decoding process (especially for large files),
we apply chunked RLNC (i.e., RLNC is applied within
non-overlapping subsets of the file). In our work, we
show the optimality of the Least Received (LR) batch
scheduling policy with regards to the expected file
transfer completion time. The LR policy strives to keep
the receiver queues balanced. This is done by
transmitting packets (corresponding to encoded batches)
that are needed by the receivers with the shortest
queues of successfully received packets. Furthermore,
we provide formulas for the expected time for the file
transmission to all receivers using the LR batch
scheduling policy and the minimum achievable coding
window size in the case of a pre-defined delay
constraint. Moreover, we evaluate through simulations a
modification of the LR policy in a more realistic
system setting with reduced feedback from the
receivers. Finally, we provide an initial analysis and
further modifications to the LR policy for
time-correlated channels and asymmetric channels.",
acknowledgement = ack-nhfb,
articleno =    "17",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Friedlander:2019:GLC,
author =       "Eric Friedlander and Vaneet Aggarwal",
title =        "Generalization of {LRU} Cache Replacement Policy with
Applications to Video Streaming",
journal =      j-TOMPECS,
volume =       "4",
number =       "3",
pages =        "18:1--18:??",
month =        sep,
year =         "2019",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3345022",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sat Sep 21 07:21:17 MDT 2019",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/citation.cfm?id=3345022",
abstract =     "Caching plays a crucial role in networking systems to
reduce the load on the network and is commonly employed
by content delivery networks (CDNs) to improve
performance. One of the commonly used mechanisms, Least
Recently Used (LRU), works well for identical file
sizes. However, for asymmetric file sizes, the
adaptation to the LRU strategy, called gLRU, where the
file is sub-divided into equal-sized chunks. In this
strategy, a chunk of the newly requested file is added
in the cache, and a chunk of the least-recently-used
file is removed from the cache. Even though approximate
analysis for the hit rate has been studied for LRU, the
analysis does not extend to gLRU, since the metric of
interest is no longer the hit rate as the cache has
approximation analysis for this policy where the cache
may have partial file contents. The approximation
approach is validated by simulations. Further, gLRU
outperforms the LRU strategy for a Zipf file popularity
distribution and censored Pareto file size distribution
applications can further use the partial cache contents
to help the stall duration significantly, and the
numerical results indicate significant improvements
(32\%) in stall duration using the gLRU strategy as
compared to the LRU strategy. Furthermore, the gLRU
replacement policy compares favorably to two other
cache replacement policies when simulated on MSR
Cambridge Traces obtained from the SNIA IOTTA
repository.",
acknowledgement = ack-nhfb,
articleno =    "18",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "http://dl.acm.org/pub.cfm?id=J1525",
}

@Article{Kalbasi:2019:AAM,
author =       "Amir Kalbasi and Diwakar Krishnamurthy and Jerry
Rolia",
title =        "{AMIR}: Analytic Method for Improving Responsiveness
by Reducing Burstiness",
journal =      j-TOMPECS,
volume =       "4",
number =       "4",
pages =        "19:1--19:36",
month =        dec,
year =         "2019",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3365669",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Mar 19 13:56:10 MDT 2020",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/doi/abs/10.1145/3365669",
abstract =     "Service demand burstiness, or serial correlations in
resource service demands, has previously been shown to
have an adverse impact on system performance metrics
analytic framework to characterize \ldots{}",
acknowledgement = ack-nhfb,
articleno =    "19",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "https://dl.acm.org/loi/tompecs",
}

@Article{Wu:2019:FAS,
author =       "Xiaohu Wu and Francesco {De Pellegrini} and Guanyu Gao
and Giuliano Casale",
title =        "A Framework for Allocating Server Time to Spot and
On-Demand Services in Cloud Computing",
journal =      j-TOMPECS,
volume =       "4",
number =       "4",
pages =        "20:1--20:31",
month =        dec,
year =         "2019",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3366682",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Mar 19 13:56:10 MDT 2020",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/doi/abs/10.1145/3366682",
abstract =     "Cloud computing delivers value to users by
needed. An approach is to provide both on-demand and
spot services on shared servers. The former allows
users to access servers on demand at a fixed price
\ldots{}",
acknowledgement = ack-nhfb,
articleno =    "20",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "https://dl.acm.org/loi/tompecs",
}

@Article{Vial:2019:RCP,
author =       "Daniel Vial and Vijay Subramanian",
title =        "On the Role of Clustering in Personalized {PageRank}
Estimation",
journal =      j-TOMPECS,
volume =       "4",
number =       "4",
pages =        "21:1--21:33",
month =        dec,
year =         "2019",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3366635",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Mar 19 13:56:10 MDT 2020",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/doi/abs/10.1145/3366635",
abstract =     "Personalized PageRank (PPR) is a measure of the
importance of a node from the perspective of another
(we call these nodes the target and the source,
respectively). PPR has been used in many applications,
such as offering a Twitter user (the source)
recommendations of whom to follow (targets deemed
important by PPR); additionally, PPR has been used in
graph-theoretic problems such as community detection.
However, computing PPR is infeasible for large networks
like Twitter, so efficient estimation algorithms are
necessary.\par

In this work, we analyze the relationship between PPR
estimation complexity and clustering. First, we devise
algorithms to estimate PPR for many source/target
pairs. In particular, we propose an enhanced version of
the existing single pair estimator Bidirectional-PPR
that is more useful as a primitive for many pair
estimation. We then show that the common underlying
graph can be leveraged to efficiently and jointly
estimate PPR for many pairs rather than treating each
pair separately using the primitive algorithm. Next, we
show the complexity of our joint estimation scheme
relates closely to the degree of clustering among the
sources and targets at hand, indicating that estimating
PPR for many pairs is easier when clustering occurs.
Finally, we consider estimating PPR when several
machines are available for parallel computation,
devising a method that leverages our clustering
findings, specifically the quantities computed in situ,
to assign tasks to machines in a manner that reduces
computation time. This demonstrates that the
relationship between complexity and clustering has
important consequences in a practical distributed
setting.",
acknowledgement = ack-nhfb,
articleno =    "21",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "https://dl.acm.org/loi/tompecs",
}

@Article{Wang:2019:CSC,
author =       "Xiaoming Wang and Di Xiao and Xiaoyong Li and Daren B.
H. Cline and Dmitri Loguinov",
title =        "Consistent Sampling of Churn Under Periodic
Non-Stationary Arrivals in Distributed Systems",
journal =      j-TOMPECS,
volume =       "4",
number =       "4",
pages =        "22:1--22:33",
month =        dec,
year =         "2019",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3368510",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Mar 19 13:56:10 MDT 2020",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/doi/abs/10.1145/3368510",
abstract =     "Characterizing user churn has become an important
research area of networks and distributed systems, both
in theoretical analysis and system design. A realistic
churn model, often measured using periodic observation,
should replicate two key properties \ldots{}",
acknowledgement = ack-nhfb,
articleno =    "22",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "https://dl.acm.org/loi/tompecs",
}

@Article{Borst:2019:LR,
author =       "Sem Borst and Carey Williamson",
title =        "List of Reviewers",
journal =      j-TOMPECS,
volume =       "4",
number =       "4",
pages =        "23:1--23:2",
month =        dec,
year =         "2019",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3369841",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Mar 19 13:56:10 MDT 2020",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/doi/abs/10.1145/3369841",
acknowledgement = ack-nhfb,
articleno =    "23",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "https://dl.acm.org/loi/tompecs",
}

@Article{Mciver:2020:ISS,
author =       "Annabelle Mciver and Andr{\'a}s Horv{\'a}th",
title =        "Introduction to the Special Section on Quantitative
Evaluation of Systems {(QEST 2018)}",
journal =      j-TOMPECS,
volume =       "5",
number =       "1",
pages =        "1:1--1:1",
month =        feb,
year =         "2020",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3376999",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Mar 19 13:56:11 MDT 2020",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/doi/abs/10.1145/3376999",
acknowledgement = ack-nhfb,
articleno =    "1",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "https://dl.acm.org/loi/tompecs",
}

@Article{Salamati:2020:LAT,
author =       "Mahmoud Salamati and Sadegh Soudjani and Rupak
Majumdar",
title =        "A {Lyapunov} Approach for Time-Bounded Reachability of
{CTMCs} and {CTMDPs}",
journal =      j-TOMPECS,
volume =       "5",
number =       "1",
pages =        "2:1--2:29",
month =        feb,
year =         "2020",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3371923",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Mar 19 13:56:11 MDT 2020",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/doi/abs/10.1145/3371923",
abstract =     "Time-bounded reachability is a fundamental problem in
model checking continuous-time Markov chains (CTMCs)
and Markov decision processes (CTMDPs) for
specifications in continuous stochastic logics. It can
be computed by numerically solving a \ldots{}",
acknowledgement = ack-nhfb,
articleno =    "2",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "https://dl.acm.org/loi/tompecs",
}

@Article{Marin:2020:DRA,
author =       "Andrea Marin and Sabina Rossi and Matteo Sottana",
title =        "Dynamic Resource Allocation in Fork--Join Queues",
journal =      j-TOMPECS,
volume =       "5",
number =       "1",
pages =        "3:1--3:28",
month =        feb,
year =         "2020",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3372376",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Mar 19 13:56:11 MDT 2020",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/doi/abs/10.1145/3372376",
abstract =     "Fork--join systems play a pivotal role in the analysis
of distributed systems, telecommunication
we consider a fork-join system consisting of $K$
parallel servers, each of which works on one \ldots{}",
acknowledgement = ack-nhfb,
articleno =    "3",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "https://dl.acm.org/loi/tompecs",
}

@Article{Yang:2020:EPC,
author =       "Jinfeng Yang and Bingzhe Li and David J. Lilja",
title =        "Exploring Performance Characteristics of the {Optane
$3$D Xpoint} Storage Technology",
journal =      j-TOMPECS,
volume =       "5",
number =       "1",
pages =        "4:1--4:28",
month =        feb,
year =         "2020",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3372783",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Mar 19 13:56:11 MDT 2020",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/doi/abs/10.1145/3372783",
abstract =     "Intel's Optane solid-state nonvolatile storage device
is constructed using their new 3D Xpoint
technology. Although it is claimed that this technology
can deliver substantial performance improvements
compared to NAND-based storage systems, its performance
characteristics have not been well studied. In this
study, intensive experiments and measurements have been
carried out to extract the intrinsic performance
characteristics of the Optane SSD, including the basic
technology, performance consistency under a highly
intensive I/O workload, influence of unaligned request
size, elimination of write-driven garbage collection,
read disturb issues, and tail latency problem. The
performance is compared to that of a conventional NAND
SSD to indicate the performance difference of the
Optane SSD in each scenario. In addition, by using
TPC-H, a read-intensive benchmark, a database system's
performance has been studied on our target storage
devices to quantify the potential benefits of the
Optane SSD to a real application. Finally, the
performance impact of hybrid Optane and NAND SSD
storage systems on a database application has been
investigated.",
acknowledgement = ack-nhfb,
articleno =    "4",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "https://dl.acm.org/loi/tompecs",
}

@Article{Rattaro:2020:QPD,
author =       "Claudina Rattaro and Laura Aspirot and Ernesto
Mordecki and Pablo Belzarena",
title =        "{QoS} Provision in a Dynamic Channel Allocation Based
journal =      j-TOMPECS,
volume =       "5",
number =       "1",
pages =        "5:1--5:29",
month =        feb,
year =         "2020",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3372786",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Mar 19 13:56:11 MDT 2020",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/doi/abs/10.1145/3372786",
abstract =     "Cognitive Radio Networks have emerged in the last
decades as a solution of two problems: spectrum
underutilization and spectrum scarcity. In this work,
we propose a dynamic spectrum sharing mechanism, where
primary users have strict priority over \ldots{}",
acknowledgement = ack-nhfb,
articleno =    "5",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "https://dl.acm.org/loi/tompecs",
}

@Article{Jiang:2020:LHS,
author =       "Qingye Jiang and Young Choon Lee and Albert Y.
Zomaya",
title =        "The Limit of Horizontal Scaling in Public Clouds",
journal =      j-TOMPECS,
volume =       "5",
number =       "1",
pages =        "6:1--6:22",
month =        feb,
year =         "2020",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3373356",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Thu Mar 19 13:56:11 MDT 2020",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/doi/abs/10.1145/3373356",
abstract =     "Public cloud users are educated to practice horizontal
scaling at the application level, with the assumption
that more processing capacity can be achieved by adding
nodes into the server fleet. In reality, however,
applications --- even those specifically \ldots{}",
acknowledgement = ack-nhfb,
articleno =    "6",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "https://dl.acm.org/loi/tompecs",
}

author =       "Mahmoud Awad and Daniel A. Menasc{\'e}",
title =        "{iModel}: Automatic Derivation of Analytic Performance
Models",
journal =      j-TOMPECS,
volume =       "5",
number =       "2",
pages =        "7:1--7:30",
month =        apr,
year =         "2020",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3374220",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sun Mar 28 07:27:06 MDT 2021",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/doi/10.1145/3374220",
abstract =     "Deriving analytic performance models requires detailed
knowledge of the architecture and behavior of the
computer system being modeled as well as modeling
skills. This detailed knowledge may not be readily
available (or it may be impractical to gather)
\ldots{}",
acknowledgement = ack-nhfb,
articleno =    "7",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "https://dl.acm.org/loi/tompecs",
}

@Article{Vassio:2020:UIO,
author =       "Luca Vassio and Michele Garetto and Carla Chiasserini
and Emilio Leonardi",
Modeling and Optimization of Ads Placement",
journal =      j-TOMPECS,
volume =       "5",
number =       "2",
pages =        "8:1--8:26",
month =        apr,
year =         "2020",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3377144",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sun Mar 28 07:27:06 MDT 2021",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/doi/10.1145/3377144",
on the impact of user interaction and response to
targeted advertising campaigns. We analytically model
the system dynamics accounting for the user behavior
and devise strategies to maximize a \ldots{}",
acknowledgement = ack-nhfb,
articleno =    "8",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "https://dl.acm.org/loi/tompecs",
}

@Article{Plakia:2020:SSS,
author =       "Maria Plakia and Evripides Tzamousis and Thomais
Asvestopoulou and Giorgos Pantermakis and Nick
Filippakis and Henning Schulzrinne and Yana Kane-Esrig
title =        "Should {I} Stay or Should {I} Go: Analysis of the
Impact of Application {QoS} on User Engagement in
journal =      j-TOMPECS,
volume =       "5",
number =       "2",
pages =        "9:1--9:32",
month =        apr,
year =         "2020",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3377873",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sun Mar 28 07:27:06 MDT 2021",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/doi/10.1145/3377873",
abstract =     "To improve the user engagement, especially under
moderate to high traffic demand, it is important to
understand the impact of the network and application
evaluates the impact of impairments, with \ldots{}",
acknowledgement = ack-nhfb,
articleno =    "9",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "https://dl.acm.org/loi/tompecs",
}

@Article{Gupta:2020:SPD,
author =       "Manu K. Gupta and N. Hemachandra and J.
Venkateswaran",
title =        "Some Parameterized Dynamic Priority Policies for
Two-Class {M/G/1} Queues: Completeness and
Applications",
journal =      j-TOMPECS,
volume =       "5",
number =       "2",
pages =        "10:1--10:37",
month =        apr,
year =         "2020",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3384390",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sun Mar 28 07:27:06 MDT 2021",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/doi/10.1145/3384390",
abstract =     "Completeness of a dynamic priority scheduling scheme
is of fundamental importance for the optimal control of
queues in areas as diverse as computer communications,
communication networks, supply/value chains, and
manufacturing systems. Our first main \ldots{}",
acknowledgement = ack-nhfb,
articleno =    "10",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "https://dl.acm.org/loi/tompecs",
}

@Article{Sieber:2020:SAU,
author =       "Christian Sieber and Susanna Schwarzmann and Andreas
Blenk and Thomas Zinner and Wolfgang Kellerer",
title =        "Scalable Application- and User-aware Resource
Allocation in Enterprise Networks Using End-Host
Pacing",
journal =      j-TOMPECS,
volume =       "5",
number =       "3",
pages =        "11:1--11:41",
month =        nov,
year =         "2020",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3381996",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sun Mar 28 07:27:06 MDT 2021",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/doi/10.1145/3381996",
abstract =     "Providing scalable user- and application-aware
resource allocation for heterogeneous applications
sharing an enterprise network is still an unresolved
problem. The main challenges are as follows: (i) How do
we define user- and application-aware shares \ldots{}",
acknowledgement = ack-nhfb,
articleno =    "11",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "https://dl.acm.org/loi/tompecs",
}

@Article{Costa:2020:ENN,
author =       "Georges DA Costa and Jean-Marc Pierson and Leandro
Fontoura-Cupertino",
title =        "Effectiveness of Neural Networks for Power Modeling
for Cloud and {HPC}: It's Worth It!",
journal =      j-TOMPECS,
volume =       "5",
number =       "3",
pages =        "12:1--12:36",
month =        nov,
year =         "2020",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3388322",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sun Mar 28 07:27:06 MDT 2021",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/doi/10.1145/3388322",
abstract =     "Power consumption of servers and applications are of
utmost importance as computers are becoming ubiquitous,
from smart phones to IoT and full-fledged computers. To
optimize their power consumption, knowledge is
necessary during execution at different \ldots{}",
acknowledgement = ack-nhfb,
articleno =    "12",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "https://dl.acm.org/loi/tompecs",
}

@Article{Liao:2020:TEB,
author =       "Jianwei Liao and Zhibing Sha and Zhigang Cai and
Zhiming Liu and Kenli Li and Wei-Keng Liao and Alok N.
Choudhary and Yutaka Ishiakwa",
title =        "Toward Efficient Block Replication Management in
Distributed Storage",
journal =      j-TOMPECS,
volume =       "5",
number =       "3",
pages =        "13:1--13:27",
month =        nov,
year =         "2020",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3412450",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sun Mar 28 07:27:06 MDT 2021",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/doi/10.1145/3412450",
abstract =     "Distributed/parallel file systems commonly suffer from
load imbalance and resource contention due to the
bursty characteristic exhibited in scientific
supporting dynamic block data replication and
\ldots{}",
acknowledgement = ack-nhfb,
articleno =    "13",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "https://dl.acm.org/loi/tompecs",
}

@Article{Raeis:2020:AQM,
author =       "Majid Raeis and Almut Burchard and J{\"o}rg
Liebeherr",
title =        "Analysis of a Queueing Model for Energy Storage
Systems with Self-discharge",
journal =      j-TOMPECS,
volume =       "5",
number =       "3",
pages =        "14:1--14:26",
month =        nov,
year =         "2020",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3422711",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sun Mar 28 07:27:06 MDT 2021",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/doi/10.1145/3422711",
proposed queueing system model for energy storage with
discharge. Even without a load, energy storage systems
experience a reduction of the stored energy through
self-discharge. In some storage \ldots{}",
acknowledgement = ack-nhfb,
articleno =    "14",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "https://dl.acm.org/loi/tompecs",
}

@Article{Golubchik:2021:MNE,
author =       "Leana Golubchik",
title =        "A Message from the New {Editor-in-Chief}",
journal =      j-TOMPECS,
volume =       "5",
number =       "4",
pages =        "15:1--15:1",
month =        mar,
year =         "2021",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3432597",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sun Mar 28 07:27:07 MDT 2021",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/doi/10.1145/3432597",
acknowledgement = ack-nhfb,
articleno =    "15",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "https://dl.acm.org/loi/tompecs",
}

@Article{Cruz:2021:OTD,
author =       "Eduardo H. M. Cruz and Matthias Diener and La{\'e}rcio
L. Pilla and Philippe O. A. Navaux",
title =        "Online Thread and Data Mapping Using a Sharing-Aware
Memory Management Unit",
journal =      j-TOMPECS,
volume =       "5",
number =       "4",
pages =        "16:1--16:28",
month =        mar,
year =         "2021",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3433687",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sun Mar 28 07:27:07 MDT 2021",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/doi/10.1145/3433687",
abstract =     "Current and future architectures rely on thread-level
parallelism to sustain performance growth. These
architectures have introduced a complex memory
hierarchy, consisting of several cores organized
hierarchically with multiple cache levels and NUMA
\ldots{}",
acknowledgement = ack-nhfb,
articleno =    "16",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "https://dl.acm.org/loi/tompecs",
}

@Article{Al-Abbasi:2021:VJS,
author =       "Abubakr O. Al-Abbasi and Vaneet Aggarwal",
title =        "{VidCloud}: Joint Stall and Quality Optimization for
Video Streaming over Cloud",
journal =      j-TOMPECS,
volume =       "5",
number =       "4",
pages =        "17:1--17:32",
month =        mar,
year =         "2021",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3442187",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sun Mar 28 07:27:07 MDT 2021",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/doi/10.1145/3442187",
abstract =     "As video-streaming services have expanded and
improved, cloud-based video has evolved into a
necessary feature of any successful business for
reaching internal and external audiences. In this
article, video streaming over distributed storage is
\ldots{}",
acknowledgement = ack-nhfb,
articleno =    "17",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "https://dl.acm.org/loi/tompecs",
}

@Article{Makrani:2021:APM,
author =       "Hosein Mohamamdi Makrani and Hossein Sayadi and Najmeh
Nazari and Sai Mnoj Pudukotai Dinakarrao and Avesta
Sasan and Tinoosh Mohsenin and Setareh Rafatirad and
Houman Homayoun",
title =        "Adaptive Performance Modeling of Data-intensive
Workloads for Resource Provisioning in Virtualized
Environment",
journal =      j-TOMPECS,
volume =       "5",
number =       "4",
pages =        "18:1--18:24",
month =        mar,
year =         "2021",
CODEN =        "????",
DOI =          "https://doi.org/10.1145/3442696",
ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L =       "2376-3639",
bibdate =      "Sun Mar 28 07:27:07 MDT 2021",
bibsource =    "http://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL =          "https://dl.acm.org/doi/10.1145/3442696",
abstract =     "The processing of data-intensive workloads is a
challenging and time-consuming task that often requires
massive infrastructure to ensure fast data analysis.
The cloud platform is the most popular and powerful
scale-out infrastructure to perform big data \ldots{}",
acknowledgement = ack-nhfb,
articleno =    "18",
fjournal =     "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL =  "https://dl.acm.org/loi/tompecs",
}