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A Game Theory Approach to Fair and Efficient Resource Allocation in Cloud Computing

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  • Xin Xu
  • Huiqun Yu

Abstract

On-demand resource management is a key characteristic of cloud computing. Cloud providers should support the computational resource sharing in a fair way to ensure that no user gets much better resources than others. Another goal is to improve the resource utilization by minimizing the resource fragmentation when mapping virtual machines to physical servers. The focus of this paper is the proposal of a game theoretic resources allocation algorithm that considers the fairness among users and the resources utilization for both. The experiments with an FUGA implementation on an 8-node server cluster show the optimality of this algorithm in keeping fairness by comparing with the evaluation of the Hadoop scheduler. The simulations based on Google workload trace demonstrate that the algorithm is able to reduce resource wastage and achieve a better resource utilization rate than other allocation mechanisms.

Suggested Citation

  • Xin Xu & Huiqun Yu, 2014. "A Game Theory Approach to Fair and Efficient Resource Allocation in Cloud Computing," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-14, April.
    • Handle: RePEc:hin:jnlmpe:915878
    DOI: 10.1155/2014/915878
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    Cited by:

    1. Pejman Goudarzi & Mehdi Hosseinpour & Roham Goudarzi & Jaime Lloret, 2022. "Holistic Utility Satisfaction in Cloud Data Centre Network Using Reinforcement Learning," Future Internet, MDPI, vol. 14(12), pages 1-21, December.

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