ACM Transactions on

Modeling and Performance Evaluation of Computing Systems (TOMPECS)

Latest Articles

Scheduling Storms and Streams in the Cloud

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,... (more)


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... (more)

Analysis of an Offloading Scheme for Data Centers in the Framework of Fog Computing

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... (more)

A Control-Theoretic Analysis of Low-Priority Congestion Control Reprioritization under AQM

Recently, a negative interplay has been shown to arise when scheduling/Active Queue Management (AQM) techniques and low-priority congestion control... (more)

A Truthful Incentive Mechanism for Emergency Demand Response in Geo-Distributed Colocation Data Centers

Data centers are key participants in demand response programs, including emergency demand response... (more)

False-Positive Probability and Compression Optimization for Tree-Structured Bloom Filters

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... (more)



ACM Transactions on Modeling and Performance Evaluation of Computing Systems (ToMPECS) is a new ACM journal that publishes refereed articles on all aspects of the modeling, analysis, and performance evaluation of computing and communication systems.

The target areas for the application of these performance evaluation methodologies are broad, and include traditional areas such as computer networks, computer systems, storage systems, telecommunication networks, and Web-based systems, as well as new areas such as data centers, green computing/communications, energy grid networks, and on-line social networks.

Issues of the journal will be published on a quarterly basis, appearing both in print form and in the ACM Digital Library. The first issue will likely be released in late 2015 or early 2016.


New options for ACM authors to manage rights and permissions for their work

ACM introduces a new publishing license agreement, an updated copyright transfer agreement, and a new author-pays option which allows for perpetual open access through the ACM Digital Library. For more information, visit the ACM Author Rights webpage.

Forthcoming Articles
Fairness and Incentive Considerations in Energy Apportionment Policies

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 to reveal the contribution of each entity to the total consumption and for energy management. Unfortunately, energy accounting inherits the cost allocation or apportionment problem of accounting in general, which does not have a general single best solution. In this paper we leverage cooperative game theory commonly used in cost allocation problems to study the energy apportionment problem, i.e., the problem of dividing the actual energy consumption of a system among the consuming entities (e.g., applications, processes or users of the system). We present a detailed categorisation and analysis of eight previously proposed energy apportionment policies from different fields in computer and communication systems and propose two novel apportionment policies based on cooperative game theory. Our comparative analysis in terms of information requirement, computational complexity and fairness (which relates to incentives) shows that there is a trade-off between fairness and the other evaluation criteria. We provide guidelines to select an energy apportionment policy depending on the characteristics of the system.

Measurement, Modeling, and Analysis of the Mobile App Ecosystem

Mobile applications (apps) have been gaining rising 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, to which we refer 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.

On Fair Attribution of Costs Under Peak-based Pricing to Cloud Tenants

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 relationship offered to tenants. To overcome these shortcomings, we define an alternative pricing scheme to more fairly distribute a cloud's costs among its tenants. Our approach to fair attribution of cloud's costs is inspired by the concept of Shapley values used to fairly divide revenue among participants of a financial coalition. We demonstrate the efficacy of our scheme under price-sensitive tenant demand response using a combination of (i) extensive empirical evaluation with recent data center 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.

File dissemination in dynamic graphs: The case of independent and correlated links in series

In this paper 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 in ad hoc delay/tolerant networks where edges are subject to intermittent unavailability.

Self-similarity in Social Network Dynamics

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, i.e., 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 process, through analysis of two detailed, time-stamped traces, a 199 million edge trace over two years in the Renren social network, and 876K interactions in a four 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.

Detecting Sponsored Recommendations

With a vast number of items, web-pages, and news to choose from, online services and the customers both benefit tremendously from personalized recommender systems. Such systems however provide great opportunities for targeted advertisements, by displaying ads alongside genuine recommendations. We consider a {\em biased} recommendation system where 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 a bias. We propose an algorithm that can detect such a 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 sufficient number of users provide feedback on sufficient number of recommendations. We provide extensive simulations with real data sets and practical recommender systems, which confirm the trade offs in the theoretical guarantees.

All ACM Journals | See Full Journal Index