Performance Analysis of different Task Scheduling Algorithms in Cloud Computing under Dynamic Environment

Abstract

Cloud computing is one of the emerging technology in the field of computer science in which services are provided through the internet on-demand. It has various features like simple to use, minimum power consumption, and minimum cost. Cloud environment contains various virtualized computing resources, various development platforms, and users jobs. Task scheduling is one of the main essential parts of the cloud environment which is mainly concerned with minimizing energy consumption and maximizing resource utilization. In this paper, we analyze seven popular job scheduling algorithms i.e. FCFS, SJF, Round Robin, Min-Min, Max-Min, Genetic algorithm, and Ant colony optimization. The comparative analysis of the algorithms is based on response time and makespan. The analysis results show that the Genetic algorithm outperforms in terms of response time and Ant Colony Optimization is performed better in terms of makespan.