Dynamic Virtual Machine Allocation in Cloud Computing Using Elephant Herd Optimization Scheme

Abstract

Cloud computing is a computing technology that is expeditiously evolving. Cloud is a type of distributed computing system that provides a scalable computational resource on demand including storage, processing power and applications as a service via Internet. Cloud computing, with the assistance of virtualization, allows for transparent data and service sharing across cloud users, as well as access to thousands of machines in a single event. Virtual machine (VM) allocation is a difficult job in virtualization that is governed as an important aspect of VM migration. This process is performed to discover the optimum way to place VMs on physical machines (PMs) since it has clear implications for resource usage, energy efficiency, and performance of several applications, among other things. Hence an efficient VM placement problem is required. This paper presents a VM allocation technique based on the elephant herd optimization scheme. The proposed method is evaluated using real-time workload traces and the empirical results show that the proposed method reduces energy consumption, and maximizes resource utilization when compared to the existing methods.

Enhancing Harris Hawks Optimization Algorithm for Resource Allocation in Cloud Computing Environments

“…Empirical evidence demonstrates that the E-MBFD methodology surpasses traditional methods in terms of reduced SLA violations and decreased power consumption. A VM allocation method using the elephant herd optimization scheme was presented by Madhusudhan, et al [27]. Upon conducting tests on real-time workloads, the methodology demonstrated substantial energy and resource utilization enhancements versus conventional approaches.…”

A Harris Hawk Optimisation system for energy and resource efficient virtual machine placement in cloud data centers

“…In [25] a clonal optimization model is proposed for resource allocation for cloud infrastructure to improve power efficiency in cloud. In recent years many other work are been proposed using nature inspired algorithms like work inspired from BAT algorithm [26], jelly fish [27], wild horse [28], FOX inspired model [29], Elephant herd [30], Whale Optimization Algorithm (WOA) [31], Ant Lion [32], Butterfly Optimization Algorithm (BOA) [33] and Gray Wolf Optimization (GWO) [34].…”