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%%% -*-BibTeX-*-
%%% ====================================================================
%%%  BibTeX-file{
%%%     author          = "Nelson H. F. Beebe",
%%%     version         = "1.01",
%%%     date            = "14 October 2017",
%%%     time            = "10:27:10 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        = "01660 1666 9361 88369",
%%%     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",
%%%     license         = "public domain",
%%%     supported       = "yes",
%%%     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 1.01, the COMPLETE journal
%%%                        coverage looked like this:
%%%
%%%                             2016 (  26)    2017 (   6)
%%%
%%%                             Article:         32
%%%
%%%                             Total entries:   32
%%%
%%%                        The journal Web page can be found at:
%%%
%%%                            http://tompecs.acm.org/
%%%
%%%                        The journal table of contents page is at:
%%%
%%%                            http://dl.acm.org/pub.cfm?id=J1525
%%%
%%%                        Qualified subscribers can retrieve the full
%%%                        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
%%%                        World Wide Web locations of additional
%%%                        information about the entry.
%%%
%%%                        BibTeX citation tags are uniformly chosen
%%%                        as name:year:abbrev, where name is the
%%%                        family name of the first author or editor,
%%%                        year is a 4-digit number, and abbrev is a
%%%                        3-letter condensation of important title
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%%%
%%%                        In this bibliography, entries are sorted in
%%%                        publication order, using ``bibsort -byvolume.''
%%%
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%%% ====================================================================
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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,
  author =       "Ahmad Lashgar and Amirali Baniasadi",
  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
                 development productivity. In this article, we
                 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
                 the adaptation has not been adequately analyzed. We
                 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
                 Arjomand and Hamid Sarbazi-Azad",
  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
                 freedom are used in allocation strategy. This article
                 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
                 overheads of garbage collection. Our steady-state
                 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
                 proposed in the literature. In this article, we analyze
                 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
                 helpful in understanding the trade-off between
                 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
                 of effort. In this article, we design a class of
                 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",
}

@Article{Ghaderi:2016:SSS,
  author =       "Javad Ghaderi and Sanjay Shakkottai and R. Srikant",
  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
                 paradigms (e.g., Twitter Storm, Streaming MapReduce),
                 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",
}

@Article{Papadopoulos:2016:PPE,
  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.
                 In this article, we present a Performance Evaluation
                 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",
  title =        "Analysis of an Offloading Scheme for Data Centers in
                 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
                 in-memory database systems. In this article, we apply
                 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
                 single best solution. In this article, we leverage
                 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
                 electric utilities) do not adequately address these
                 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",
  abstract =     "In this article, we investigate the traversal time of
                 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",
  title =        "Detecting Sponsored Recommendations",
  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
                 opportunities for targeted advertisements by displaying
                 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
                 provide a new way for developing, downloading, and
                 updating software applications in modern mobile
                 devices, have recently emerged. To better understand
                 the download patterns, popularity trends, and
                 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
                 be mainly attributed to a new download pattern, which
                 we refer to as the clustering effect. We validate the
                 existence of this effect by revealing a strong temporal
                 affinity of user downloads to app categories. Based on
                 these observations, we propose a new formal clustering
                 model for the distribution of app downloads and
                 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
                 cloud workload consolidation mechanism, substantiating
                 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
                 difficult to describe. In this article, we introduce C
                 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",
  title =        "Advance Reservation Games",
  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
                 answers, but tracing online how much better the answers
                 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
                 high-priority queries, our adaptive controller had 55\%
                 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",
}