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ACM Transactions on

Modeling and Performance Evaluation of Computing Systems (TOMPECS)

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Measurement, Modeling, and Analysis of the Mobile App Ecosystem

Mobile applications (apps) have been gaining popularity due to the advances in mobile technologies and the large increase in the number of mobile... (more)

Cocoa

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... (more)

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

Modern memory systems are structured under hierarchy and concurrency. The combined impact of hierarchy and concurrency, however, is application... (more)

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About TOMPECS

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.

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Forthcoming Articles
Efficient Redundancy Techniques for Latency Reduction in Cloud Systems

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 thus reduce average 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 understand when and how redundancy gives a cost-efficient reduction in latency. For a general task service time distribution, we compare different redundancy strategies, for e.g. the number of redundant tasks, and time when they are issued and canceled. We get the insight that the log-concavity of the task service distribution is a key factor in determining whether adding redundancy helps. If the service distribution is log-convex, then adding maximum redundancy reduces both latency and cost. And if it is log-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 distribution. This work also generalizes and extends some results in the analysis of fork-join queues.

Advance Reservation Games

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 making 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 yielding 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.

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