Utilization-aware energy-efficient virtual machine placement in cloud networks using NSGA-III meta-heuristic approach

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.