K-mMA VM selection in dynamic VM consolidation for improving energy efficiency at cloud data centre

Int. J. Commun. Networks Distributed Syst. Pub Date : 2018-08-27 DOI:10.1504/IJCNDS.2018.10014504

G. F. Shidik, Azhari, K. Mustofa

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引用次数: 2

Abstract

Dynamic virtual machine (VM) consolidation is an alternative solution for managing and optimising energy efficiency in a cloud data centre. This research proposed VM selection method in dynamic VM consolidation based on K-means clustering technique and computational model Markov normal algorithm (K-mMA). The objective of VM selection is to select proper VM, which should be migrated away from the overloaded physical machine and to avoid oversubscribe host. The VM selection method has been tested in simulation condition using CloudSim and PlanetLabs datasets with various conditions of VM instances (homogeneous and heterogeneous). The performance metrics in this research are energy consumption (EC), SLA time per active host (SLATAH), performance degradation due migration (PDM) and SLA violation (SLAV). Results experiment has shown that K-mMA could improve energy efficiency and quality of service (QOS) at cloud data centre significantly. Compared with existing method such as CFS, MMT, RC, and MC the proposed K-mMA could improve efficiency energy in cloud data centre by optimising VM selection problem up to 3.9%, 6.8%, 5.5%, and 5.3% respectively.

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动态虚拟机整合中的K-mMA虚拟机选择，提高云数据中心的能源效率

动态虚拟机(VM)整合是管理和优化云数据中心能源效率的替代解决方案。本研究提出了基于k均值聚类技术和计算模型马尔可夫正态算法(K-mMA)的动态虚拟机整合中的虚拟机选择方法。虚拟机选择的目标是选择合适的虚拟机，这些虚拟机应该从过载的物理机中迁移出去，避免过度订阅主机。虚拟机选择方法已经在模拟条件下使用CloudSim和PlanetLabs数据集进行了测试，这些数据集具有各种虚拟机实例条件(同质和异构)。本研究中的性能指标包括能耗(EC)、每台活动主机SLA时间(SLATAH)、迁移导致的性能下降(PDM)和SLA违反(SLAV)。实验结果表明，K-mMA可以显著提高云数据中心的能源效率和服务质量(QOS)。与现有的CFS、MMT、RC和MC方法相比，K-mMA方法对虚拟机选择问题的优化程度分别达到3.9%、6.8%、5.5%和5.3%，可提高云数据中心的效率能量。

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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Int. J. Commun. Networks Distributed Syst.

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