Energy efficient allocation of virtual machines in cloud computing environments based on demand forecast

  • Authors:

  • Jian Cao;Yihua Wu;Minglu Li

  • Affiliations:

  • Shanghai Key Laboratory of Scalable Computing and Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Shanghai Key Laboratory of Scalable Computing and Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Shanghai Key Laboratory of Scalable Computing and Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

  • Venue:
  • GPC'12 Proceedings of the 7th international conference on Advances in Grid and Pervasive Computing
  • Year:
  • 2012

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Abstract

In cloud computing environments, demands from different users are often handled on virtual machines (VMs) which are deployed over plenty of hosts. Huge amount of electrical power is consumed by these hosts and auxiliary infrastructures that support them. However, demands are usually time-variant and of some seasonal pattern. It is possible to reduce power consumption by forecasting varying demands periodically and allocating VMs accordingly. In this paper, we propose a power-saving approach based on demand forecast for allocation of VMs. First of all, we forecast demands of next period with Holt-Winters' exponential smoothing method. Second, a modified knapsack algorithm is used to find the appropriate allocation between VMs and hosts. Third, a self-optimizing module updates the values of parameters in Holt-Winters' model and determines the reasonable forecast frequency. We carried out a set of experiments whose results indicate that our approach can reduce the frequency of switching on/off hosts. In comparison with other approaches, this method leads to considerable power saving for cloud computing environments.