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A Strength Pareto Evolutionary Algorithm for Live Migration of Multiple Interdependent Virtual Machines in Data Centers

机译:用于数据中心中多个相互依赖的虚拟机的实时迁移的强度帕累托进化算法

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Although live VM migration has been intensively studied, the problem of live migration of multiple interdependent VMs has hardly been investigated. The most important problem in the live migration of multiple interdependent VMs is how to schedule VM migrations as the schedule will directly affect the total migration time and the total downtime of those VMs. Aiming at minimizing both the total migration time and the total downtime simultaneously, this paper presents a Strength Pareto Evolutionary Algorithm 2 (SPEA2) for the multi-VM migration scheduling problem. The SPEA2 has been evaluated by experiments, and the experimental results show that the SPEA2 can generate a set of VM migration schedules with a shorter total migration time and a shorter total downtime than an existing genetic algorithm, namely Random Key Genetic Algorithm (RKGA). This paper also studies the scalability of the SPEA2.
机译:尽管已经对实时VM迁移进行了深入研究,但是几乎没有研究过多个相互依赖的VM的实时迁移问题。实时迁移多个相互依赖的VM时,最重要的问题是如何计划VM迁移,因​​为时间表将直接影响这些VM的总迁移时间和总停机时间。为了同时减少总迁移时间和总停机时间,本文提出了针对多VM迁移调度问题的强度帕累托进化算法2(SPEA2)。通过实验对SPEA2进行了评估,实验结果表明,与现有的遗传算法,即随机密钥遗传算法(RKGA)相比,SPEA2可以生成一组VM迁移计划,该迁移计划具有更短的总迁移时间和更短的总停机时间。本文还研究了SPEA2的可伸缩性。

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