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Mathematical Problems in Engineering
Volume 2014, Article ID 613719, 11 pages
Research Article

Dynamic Placement of Virtual Machines with Both Deterministic and Stochastic Demands for Green Cloud Computing

College of Computer and Control Engineering, Nankai University, Tianjin 300071, China

Received 30 April 2014; Accepted 17 June 2014; Published 7 July 2014

Academic Editor: Yoshinori Hayafuji

Copyright © 2014 Wenying Yue and Qiushuang Chen. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Cloud computing has come to be a significant commercial infrastructure offering utility-oriented IT services to users worldwide. However, data centers hosting cloud applications consume huge amounts of energy, leading to high operational cost and greenhouse gas emission. Therefore, green cloud computing solutions are needed not only to achieve high level service performance but also to minimize energy consumption. This paper studies the dynamic placement of virtual machines (VMs) with deterministic and stochastic demands. In order to ensure a quick response to VM requests and improve the energy efficiency, a two-phase optimization strategy has been proposed, in which VMs are deployed in runtime and consolidated into servers periodically. Based on an improved multidimensional space partition model, a modified energy efficient algorithm with balanced resource utilization (MEAGLE) and a live migration algorithm based on the basic set (LMABBS) are, respectively, developed for each phase. Experimental results have shown that under different VMs’ stochastic demand variations, MEAGLE guarantees the availability of stochastic resources with a defined probability and reduces the number of required servers by 2.49% to 20.40% compared with the benchmark algorithms. Also, the difference between the LMABBS solution and Gurobi solution is fairly small, but LMABBS significantly excels in computational efficiency.