On demand Virtual Machine allocation and migration at cloud data center using Hybrid of Cat Swarm Optimization and Genetic Algorithm

N. Sharma, R. R. Guddeti
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引用次数: 9

Abstract

This paper deals with the energy saving at the data center using energy aware Virtual Machines (VMs) allocation and migration. The multi-objective based VMs allocation using Hybrid Genetic Cat Swarm Optimization (HGACSO) algorithm saves the energy consumption as well as also reduces resource wastage. Further consolidating VMs onto the minimal number of Physical Machines (PMs) using energy efficient VMs migration, we can shut down idle PMs for enhancing the energy efficiency at a cloud data center. The experimental results show that our proposed HGACSO VM allocation and energy efficient VM migration techniques achieved the energy efficiency and minimization of resource wastage.
基于Cat群优化和遗传算法的云数据中心虚拟机按需分配和迁移
本文讨论了在数据中心使用节能感知的虚拟机(vm)分配和迁移的节能问题。基于混合遗传猫群优化(HGACSO)算法的多目标虚拟机分配既节省了能量消耗,又减少了资源浪费。通过高效节能的虚拟机迁移,进一步将虚拟机整合到最少数量的物理机(pm)上,我们可以关闭闲置的pm,以提高云数据中心的能源效率。实验结果表明,我们提出的HGACSO虚拟机分配和节能虚拟机迁移技术达到了节能和最小化资源浪费的目的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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