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Cost-Efficient Server Provisioning for Cloud Gaming

Published:27 June 2018Publication History
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Abstract

Cloud gaming has gained significant popularity recently due to many important benefits such as removal of device constraints, instant-on, and cross-platform. The properties of intensive resource demands and dynamic workloads make cloud gaming appropriate to be supported by an elastic cloud platform. Facing a large user population, a fundamental problem is how to provide satisfactory cloud gaming service at modest cost. We observe that the software storage cost could be substantial compared to the server running cost in cloud gaming using elastic cloud resources. Therefore, in this article, we address the server provisioning problem for cloud gaming to optimize both the server running cost and the software storage cost. We find that the distribution of game software among servers and the selection of server types both trigger tradeoffs between the software storage cost and the server running cost in cloud gaming. We formulate the problem with a stochastic model and employ queueing theory to conduct a solid theoretical analysis of the system behaviors under different request dispatching policies. We then propose several classes of algorithms to approximate the optimal solution. The proposed algorithms are evaluated by extensive experiments using real-world parameters. The results show that the proposed Ordered and Genetic algorithms are computationally efficient, nearly cost-optimal, and highly robust to dynamic changes.

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        • Published in

          cover image ACM Transactions on Multimedia Computing, Communications, and Applications
          ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 14, Issue 3s
          Special Section on Delay-Sensitive Video Computing in the Cloud and Special Section on Extended MMSys-NOSSDAV Best Papers
          June 2018
          317 pages
          ISSN:1551-6857
          EISSN:1551-6865
          DOI:10.1145/3233173
          Issue’s Table of Contents

          Copyright © 2018 ACM

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 27 June 2018
          • Accepted: 1 February 2018
          • Revised: 1 December 2017
          • Received: 1 August 2017
          Published in tomm Volume 14, Issue 3s

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