In today’s world of information technology, cloud computing emerges as a new computing paradigm due to its economical and operational benefits. Cloud computing is able to perform the process of the enormous amount of data using high computing capacity and distributed services. When a user needs to fluctuate, cloud server capacity scales up and down to fit. It is highly flexible, reduce capital expenditure, robust disaster recovery and can operate from anywhere through the internet. The User can avail these facilities by submitting their computing task to the cloud system. So scheduling tasks to reduce the task completion time is the main purpose of task scheduling algorithm. The objective of this paper is to analyse the various parameters of particle swarm optimization (PSO) algorithm to highlight effectiveness, strength and weakness compare to other evolutionary algorithms. This paper also analyses effective virtualization of cloud infrastructure and suitability of these parameters towards efficient task scheduling in a cloud computing environment.