A systematic review of nature inspired load balancing algorithm in heterogeneous cloud computing environment

Abstract

Cloud computing is a model to handle large scale distributed and grid computing by using virtualization. It empowered the client to acquire and manage the configurable shared pool of resources by their own or with minimum interference of service provider. However, Cloud services have heterogeneous Virtual Machines (VMs), with specified configuration and oscillate resources utilization, hosted on various servers situated different geographical location which may lead to imbalanced among resource and task scheduling. This results in poor performance, inefficient energy consumption, violation of service level agreement (SLAs), and instability in system. To overcome these issues, Load balancing plays vital role. The aim of load balancing algorithms is to improve the resource utilization, energy saving and deduction of carbon emission. In this paper, we have surveyed the nature inspired load balancing algorithm such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Genetic Algorithm (GA), BAT. Our main focus in on Resource Utilization and Energy Saving.