ACM Transactions on

Modeling and Performance Evaluation of Computing Systems (TOMPECS)

Latest Articles

Contention-Aware Workload Placement for In-Memory Databases in Cloud Environments

Big data processing is driven by new types of in-memory database systems. In this article, we apply performance modeling to efficiently optimize... (more)

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... (more)

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... (more)

File Dissemination in Dynamic Graphs

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... (more)

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, 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... (more)

Detecting Sponsored Recommendations

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... (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
Cocoa: Dynamic Container-based Group Buying Strategies for Cloud Computing

Although the Infrastructure-as-a-Service 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, 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 existing cloud workload consolidation mechanism, substantiating the feasibility of group buying in cloud computing.

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.

Evaluating the Combined Effect of Memory Capacity and Concurrency for Many-core Design

Modern memory system includes features of hierarchy and concurrency, the combined impact of which is difficult to describe. In this paper, we propose $C^2$-Bound, a data-driven analytical model, that incorporates both memory capacity and data access concurrency factors to optimize many-core design. C^2-Bound is characterized by combining the newly proposed latency model, concurrent average memory access time (C-AMAT), with 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 concurrency and memory capacity, $C^2$-Bound model finds memory bound factors significantly impact the optimal number of cores as well as their optimal silicon area allocations, especially for data-intensive applications with an none parallelizable sequential portion. Therefore, our model is valuable to the design of new generation many-core architectures that target big data processing, where working sets are usually larger than the conventional scientific computing.

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