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Enhanced cuckoo search algorithm for virtual machine placement in cloud data centres

机译:用于在云数据中心中放置虚拟机的增强型杜鹃搜索算法

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>In order to enhance resource utilisation and power efficiency in cloud data centres it is important to perform Virtual Machine (VM) placement in an optimal manner. VM placement uses the method of mapping virtual machines to physical machines (PM). Cloud computing researchers have recently introduced various meta-heuristic algorithms for VM placement considering the optimised energy consumption. However, these algorithms do not meet the optimal energy consumption requirements. This paper proposes an Enhanced Cuckoo Search (ECS) algorithm to address the issues with VM placement focusing on the energy consumption. The performance of the proposed algorithm is evaluated using three different workloads in CloudSim tool. The evaluation process includes comparison of the proposed algorithm against the existing Genetic Algorithm (GA), Optimised Firefly Search (OFS) algorithm, and Ant Colony (AC) algorithm. The comparision results illustrate that the proposed ECS algorithm consumes less energy than the participant algorithms while maintaining a steady performance for SLA and VM migration. The ECS algorithm consumes around 25% less energy than GA, 27% less than OFS, and 26% less than AC.
机译:>为了提高云数据中心的资源利用率和能效,以最佳方式执行虚拟机(VM)放置很重要。 VM放置使用将虚拟机映射到物理机(PM)的方法。考虑到优化的能耗,云计算研究人员最近针对VM的部署引入了各种元启发式算法。但是,这些算法不能满足最佳能耗要求。本文提出了一种增强的布谷鸟搜索(ECS)算法,以解决虚拟机布置中的能耗问题。在CloudSim工具中使用三种不同的工作负载评估了所提出算法的性能。评估过程包括将提出的算法与现有的遗传算法(GA),优化的萤火虫搜索(OFS)算法和蚁群(AC)算法进行比较。比较结果表明,提出的ECS算法比参与算法消耗更少的能量,同时保持了SLA和VM迁移的稳定性能。 ECS算法的能耗比GA少25%,比OFS少27%,比AC低26%。

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