Utilization-aware energy-efficient virtual machine placement in cloud networks using NSGA-III meta-heuristic approach
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Utilization-aware energy-efficient virtual machine placement in cloud networks using NSGA-III meta-heuristic approach Elnaz Parvizi1 • Mohammad Hossein Rezvani1 Received: 10 September 2019 / Revised: 2 December 2019 / Accepted: 27 January 2020 Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract One of the most important issues in the context of cloud computing concerns the placement of virtual machines (VMs). The purpose of multi-objective virtual machine placement (MO-VMP) is to find the best place of VMs on physical machines (PMs) so as to reach predetermined goals. In this regard, a fundamental goal is maximizing the utilization of available resources while minimizing energy consumption. It is clear that inefficient use of computing resources (for instance CPU, memory, storage capacity, and bandwidth) could cause increased energy wastage. On the other hand, with optimal placement of VMs on PMs, one may prevent migrating them from one PM to another in the future, itself a secondary cause of increased energy consumption. Concerning the MO-VMP, there are very serious challenges in previous studies. Some of these works have attempted to minimize the number of active PMs. Others have investigated minimizing rack link traffic and optimizing communication and VM migration costs regarding routing goals. Since the MO-VMP is an NP-hard problem and involves high spatial and temporal complexities, heuristic and meta-heuristic methods have been widely used to solve the problem in the past decade. In the present research, we use the non-dominated sorting genetic algorithm (NSGA-III) to determine the optimal MO-VMP. To this end, a multi-objective optimizing problem is designed, and after introducing a non-linear convex optimization solution, we solve it with the NSGA-III method. Our main purpose is to minimize overall resource loss while minimizing power consumption as well as decreasing the number of active PMs. The simulation results on the CloudSim simulator confirm the superiority of the proposed method over basic methods such as first-fit decreasing (FFD) and exact mathematical approaches in terms of significant criteria such as execution time, utilization, resource wastage, and energy consumption. Keywords Virtual machine placement (VMP) Energy consumption Multi-objective optimization Meta-heuristic method Non-dominated sorting genetic algorithm (NSGA-III)
1 Introduction The ever-growing demand for cloud services has caused an increase in size and, consequently, the power consumption of datacenters. One of the most important strategies to modify power consumption is to adopt effective energy management policies with regard to the processing load & Mohammad Hossein Rezvani [email protected] Elnaz Parvizi [email protected] 1
Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
applied to physical machines (PMs). Large-scale cloud systems increase cloud-owners’ energy consump
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