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Parallel plants collaborative growth algorithm for virtual machine migration

机译:用于虚拟机迁移的并行工厂协同增长算法

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With the development of cloud computing, more and more virtual computing resources are provided as dynamic services to achieve green and agile computing for different kinds of projects. Virtual machine migration, as one of the main technologies, has brought huge influences on the executive and collaborative efficiency of virtual machines. However, the existing research focuses primarily on management and scheduling of virtual machines. And several studies concerning virtual machine migration in service-oriented computing still have great limitations. Especially, with the expansion of the scale of collaborative computing tasks, the complexity, as well as the number of virtual machines increased significantly. When the pre-allocation of virtual machines becomes unsuitable due to some resource failures or long waiting queue, migration of virtual machine becomes imperative. Considering the influence of initial allocation, this paper proposes a new parallel plant collaborative growth algorithm (namely Parallel-PCGA) which combines efficient plant growth optimisation operator and ring topology-based parallelisation. It brings an effective balance in solution time and accuracy. Experimental results prove that Parallel-PCGA shows high performance in large-scale virtual machine migration in cloud computing.
机译:随着云计算的发展,越来越多的虚拟计算资源被作为动态服务提供,以实现针对不同类型项目的绿色敏捷计算。虚拟机迁移作为主要技术之一,已对虚拟机的执行和协作效率产生了巨大影响。但是,现有研究主要集中在虚拟机的管理和调度上。而且,有关面向服务的计算中的虚拟机迁移的一些研究仍然有很大的局限性。特别是,随着协作计算任务规模的扩大,复杂性以及虚拟机的数量都显着增加。当由于某些资源故障或等待队列较长而导致虚拟机的预分配变得不合适时,必须迁移虚拟机。考虑到初始分配的影响,本文提出了一种新的并行植物协同生长算法(即Parallel-PCGA),该算法结合了有效的植物生长优化算子和基于环拓扑的并行化。它在解决时间和准确性之间实现了有效的平衡。实验结果证明,Parallel-PCGA在云计算的大规模虚拟机迁移中显示出高性能。

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