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Energy and Migration Cost-Aware Dynamic Virtual Machine Consolidation in Heterogeneous Cloud Datacenters

机译：异构云数据中心中具有能源和迁移成本意识的动态虚拟机整合

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摘要

Energy efficiency has become one of the major concerns for today's cloud datacenters. Dynamic virtual machine (VM) consolidation is a promising approach for improving the resource utilization and energy efficiency of datacenters. However, the live migration technology that VM consolidation relies on is costly in itself, and this migration cost is usually heterogeneous as well as the datacenter. This paper investigates the following bi-objective optimization problem: how to pay limited migration costs to save as much energy as possible via dynamic VM consolidation in a heterogeneous cloud datacenter. To capture these two conflicting objectives, a consolidation score function is designed for an overall evaluation on the basis of a migration cost estimation method and an upper bound estimation method for maximal saved power. To optimize the consolidation score, a greedy heuristic and a swap operation are introduced, and an improved grouping genetic algorithm (IGGA) based on them is proposed. Lastly, empirical studies are performed, and the evaluation results show that IGGA outperforms existing VM consolidation methods.
机译：能源效率已成为当今云数据中心的主要问题之一。动态虚拟机（VM）整合是提高数据中心的资源利用率和能源效率的一种有前途的方法。但是，VM整合所依赖的实时迁移技术本身成本很高，并且这种迁移成本通常与数据中心一样是异构的。本文研究了以下双目标优化问题：如何在异构云数据中心中通过动态VM整合来支付有限的迁移成本以节省尽可能多的能源。为了捕获这两个相互矛盾的目标，在迁移成本估算方法和最大节能量的上限估算方法的基础上，设计了一个综合评分函数进行总体评估。为了优化合并得分，引入了贪婪启发式算法和交换操作，并提出了一种基于它们的改进的分组遗传算法（IGGA）。最后，进行了实证研究，评估结果表明IGGA优于现有的VM合并方法。

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