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ACM Transactions on

Modeling and Performance Evaluation of Computing Systems (TOMPECS)

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The Economics of the Cloud

This article 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... (more)

Behavioral Model of IEEE 802.15.4 Beacon-Enabled Mode Based on Colored Petri Net

The IEEE 802.15.4 standard is widely employed in power-constrained scenarios, such as Wireless Sensor Networks deployments. Therefore, modeling this... (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
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.

Bargaining Game Based Scheduling for Performance Guarantees in Cloud Computing

In this paper, we focus on request scheduling with performance guarantees of all users in cloud computing. Each cloud user submits requests with average response time requirement, and the cloud provider tries to find a scheduling scheme, i.e., allocating user requests to limited servers, such that the average response times of all cloud users can be guaranteed. We formulate the considered scenario into a cooperative game among multiple users and try to find a Nash bargaining solution (NBS), which can simultaneously satisfy all users$'$ performance demands. We first prove the existence of NBS and then analyze its computation. Specifically, for the situation when all allocating substreams are strictly positive, we propose a computational algorithm ($\mathcal{CA}$), which can find the NBS very efficiently. For the more general case, we propose an iterative algorithm ($\mathcal{IA}$), which is based on duality theory. The convergence of our proposed $\mathcal{IA}$ algorithm is also analyzed. Finally, we conduct some numerical calculations. The experimental results show that our $\mathcal{IA}$ algorithm can find an appropriate scheduling strategy and converges to a stable state very quickly.

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