Task Scheduling in Cloud Computing Environment using Evolutionary and Swarm Based Algorithm

Abstract

Cloud computing is a popular computing concept that performs processing of huge volume of data using highly accessible geographically distributed resources that can be access by user on the basis of Pay per Use policy. In the modern computing environment where the amount of data to be processed is increasing day by day, the costs involved in the transmission and execution of such amount of data is mounting significantly. So there is a requirement of appropriate scheduling of tasks which will help to manage the escalating costs of data intensive applications. Complexity class of the task scheduling problem belongs to NP-complete involving extremely large search space with correspondingly large number of potential solutions and takes much longer time to find the optimal solution. There is no readymade and well-defined methodology to solve the problems under such circumstances. However, in cloud, it is sufficient to find near optimal solution, preferably in a short period of time.

The Evolutionary and Swarm based Algorithm is a scheduling algorithm capable of locating good solutions efficiently. The algorithm could be considered as belonging to the category of “Intelligent Optimization”. This dissertation work analyzes Genetic Algorithm, Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) task scheduling algorithms in cloud computing that address the above-mentioned problem. The simulation result shows that the PSO algorithm produces the significant result and provides the better solution. General GA, ACO and PSO are implemented and tested. Evaluation results have shown that PSO seemed to be 38% better than GA and 26.5% better than ACO.