A location selection policy of live virtual machine migration for power saving and load balancing.

Green cloud data center has become a research hotspot of virtualized cloud computing architecture. And load balancing has also been one of the most important goals in cloud data centers. Since live virtual machine (VM) migration technology is widely used and studied in cloud computing, we have focus...

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Publicado en:Scientific World Journal pp. 492615 - 492616
Autores principales: Zhao, Jia, Ding, Yan, Xu, Gaochao, Hu, Liang, Dong, Yushuang, Fu, Xiaodong
Formato: Journal Article
Publicado: Wiley-Blackwell 2013
Acceso en línea:Ver este registro en EBSCOhost
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        atl: A location selection policy of live virtual machine migration for power saving and load balancing.
      aug:
        au:
          Zhao, Jia
          Ding, Yan
          Xu, Gaochao
          Hu, Liang
          Dong, Yushuang
          Fu, Xiaodong
        affil: College of Computer Science and Technology, Jilin University, Changchun, Jilin 130000, China ; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130000, China.
      sug:
        subj: Environment
      ab: Green cloud data center has become a research hotspot of virtualized cloud computing architecture. And load balancing has also been one of the most important goals in cloud data centers. Since live virtual machine (VM) migration technology is widely used and studied in cloud computing, we have focused on location selection (migration policy) of live VM migration for power saving and load balancing. We propose a novel approach MOGA-LS, which is a heuristic and self-adaptive multiobjective optimization algorithm based on the improved genetic algorithm (GA). This paper has presented the specific design and implementation of MOGA-LS such as the design of the genetic operators, fitness values, and elitism. We have introduced the Pareto dominance theory and the simulated annealing (SA) idea into MOGA-LS and have presented the specific process to get the final solution, and thus, the whole approach achieves a long-term efficient optimization for power saving and load balancing. The experimental results demonstrate that MOGA-LS evidently reduces the total incremental power consumption and better protects the performance of VM migration and achieves the balancing of system load compared with the existing research. It makes the result of live VM migration more high-effective and meaningful.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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