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ACM Transactions on

Modeling and Performance Evaluation of Computing Systems (TOMPECS)

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Latest Articles

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)

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... (more)

Obtaining and Managing Answer Quality for Online Data-Intensive Services

Online data-intensive (OLDI) services use anytime algorithms to compute over large amounts of data and respond quickly. Interactive response times are... (more)

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... (more)

NEWS

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
The Economics of the Cloud

This paper proposes a model to study the interaction of price competition and congestion in the cloud computing marketplace. Specifically, we propose a three-tier market model that captures a marketplace with users purchasing services from Software-as-a-Service (SaaS) providers, which in turn purchase computing resources from either Provider-as-a-Service (PaaS) or Infrastructure-as-a-Service (IaaS) providers. Within each level, we define and characterize market equilibria. Further, we use these characterizations to understand the relative profitability of SaaSs and PaaSs/IaaSs, and to understand the impact of price competition on the user experienced performance, i.e., the "price of anarchy" of the cloud marketplace. Our results highlight that both of these depend fundamentally on the degree to which congestion results from shared or dedicated resources in the cloud.

Resource Auto-Scaling and Sparse Content Replication for Video Storage Systems

Many video-on-demand (VoD) providers are relying on public cloud providers for video storage, access and streaming services. In this paper, we investigate how a VoD provider may make optimal bandwidth reservations from a cloud service provider to guarantee the streaming performance while paying for the bandwidth, storage and transfer cost. We propose a predictive resource auto-scaling system that dynamically books the minimum amount of bandwidth resources from multiple servers in a cloud storage system, in order to allow the VoD provider to match its short-term demand projections. We exploit the anti-correlation between the demands of different videos for statistical multiplexing to hedge the risk of under-provisioning. The optimal load direction from video channels to cloud servers without replication constraints is derived with provable performance. We further study the joint load direction and sparse content placement problem that aims to reduce bandwidth reservation cost under sparse content replication requirements. We propose several algorithms, and especially an iterative L1-norm penalized optimization procedure to efficiently solve the problem while effectively limiting the video migration overhead. The proposed system is backed up by a demand predictor that forecasts the expectation, volatility and correlation of the streaming traffic associated with different videos based on statistical learning. Extensive simulations are conducted to evaluate our proposed algorithms, driven by the real-world workload traces collected from a commercial VoD system.

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