A network-aware and power-efficient virtual machine placement scheme in cloud datacenters based on chemical reaction optimization

Abstract

In the present article, we propose a virtual machine placement (VMP) algorithm for reducing power consumption in heterogeneous cloud data centers. We propose a novel model for the estimation of power consumption of datacenter's network. The proposed model is employed to estimate power consumption of a Fat-Tree network. It calculates the traffic of each network layer and uses the results to estimate the average power consumption of each switch in the network, which is used for network power calculation. Further, we employ the chemical reaction optimization (CRO) algorithm as a meta-heuristic algorithm to obtain a power-efficient mapping of virtual machines (VMs) to physical machines (PMs). Moreover, two kinds of solution encoding schemes, namely permutation-based and grouping-based encoding schemes, were utilized for representing individuals in CRO. For each encoding scheme, we designed proper operators required by the CRO for manipulating the molecules in search of more optimal solution candidates. Additionally, we modeled VMs with east–west and north–south communications, and PMs with constrained CPU, memory, and bandwidth capacity. Our network power model is integrated into the CRO algorithms to enable the estimation of both PMs and network power consumption. We compared our proposed methods with a number of similar methods. The evaluation results indicate that the proposed methods perform well and the CRO algorithm with the grouping-based encoding outperforms the rest of the methods in terms of power consumption. The evaluation results also show the significance of network power consumption. © 2021 Elsevier B.V.