OPTIMIZATION OF LOAD BALANCING IN CLOUD USING SWARM INTELLIGENCE: A SURVEY

Abstract

Cloud computing is a process which provides on-demand and paid access to distributed resource.This cloud services is used by everybody to reduce the cost of infrastructure and maintenance, this leads to increase in load on cloud day by day.Thus balancing the load on cloud is one of the serious problems in the cloud computing.Therefore the research was carried to balance the load and the proper load balancing can reduce the energy consumption and carbon emission.Load balancing can be achieved by task scheduling, also it facilitate the efficiency on cloud.This task scheduling results in suitable allocation of best resources to the task in execution.Load balancing can be done by using Genetic Algorithm (GA) but there was a problem of complexity and convergence leads to increase in response time.Thus a new algorithm called Particle Swarm Optimization (PSO) resolves these problems.The objective of this paper is to compare a particle swarm optimization technique with round robin, Ant colony and honeybee foraging load balancing algorithm.Comparatively PSO shows a better result.