Fatemeh Alizadeh - M.A student of computer architecture, Department of Electrical and Computer Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
Shahram Jamali - Associate professor Department of Electrical and Computer Engineering, University of Mohaghegh Ardabili Ardabil, Iran
Soheila Sadeqi - Instructor Department of Electrical and Computer Engineering, University of Mohaghegh Ardabili Ardabil, Iran

Cloud computing is the growth of distributed computing, parallel computing, utility computing and grid computing, or defined as the commercial implementation of these computer science theories. One of the fundamental issues in cloud environment is the task scheduling which plays the key role of efficiency of the whole cloud computing facilities. Scheduling maps the user’s tasks to resources to be executed efficiently in order to benefit both the service providers and customers. Since the cloud task scheduling is an NP-hard optimization problem, many meta-heuristic algorithms have been proposed to solve it. In this paper a policy based on particle swarm optimization compared with genetic algorithm and FCFS, has been introduced. PSO is a population-based search algorithm based on the simulation of the social behavior of birds within the flock. The main goal in this research is minimizing the makespan and flowtime of a given tasks set. Proposed policy and two other algorithms have been simulated using Cloudsim toolkit package. The results showed that PSO performed better than genetic and FCFS algorithms

Cloud computing, task scheduling, particle swarm optimization, makespan