Multi-objective Meta-heuristic Technique for Energy Efficient Virtual Machine Placement in Cloud Computing Data Centers

Abstract

Cloud computing has emerged as an efficient and scalable solution for storing and processing a large amount of data. Cloud data centers provide resources on demand to consumers on a pay-per-use model. However, a large number of data centers are required to support the growing demand of cloud consumers. This needs to be handled in an optimized way to avoid resource wastage and ensure that more consumers can benefit from data centers. Virtualization is the technology of creating virtual versions of computers called Virtual Machines (VMs). The Virtual Machine Placement problem is a fundamental challenge in cloud computing, where the goal is to determine the optimal allocation of Virtual Machines to Physical Machines (PMs) within a data center. An efficient Virtual Machine Placement technique helps properly place VMs on PMs, significantly optimizing the number of servers, maintenance costs, CPU utilization, and power consumption. We present a novel hybrid approach that combines the Ant Colony Optimization (ACO) algorithm and the Sine Cosine Algorithm (SCA) for efficient VM placement. Since SCA is an emerging search algorithm that utilizes Sine and Cosine functions in the engineering field, it has been used to explore the solutions obtained by the ACO algorithm. The ACO algorithm has been applied to exploit the solutions of the search space for efficient VM placement, aiding in power management and minimizing resource wastage. The results have been verified by comparing the performance against other algorithms to prove that our proposed algorithm outperforms them.