Sadegh Nejatzadeh - Department of Computer Science and Engineering Shahid Beheshti University Tehran, Iran
Ali Afraz - Department of Electrical and Computer Engineering Islamic Azad University Janah Branch Janah, Iran
Saeid Malekpour - Department of Computer Engineering and Information Technology Amirkabir University of Technology Tehran, Iran

Grid computing is a promising technology for future computing platforms and is expected to provide easier access to remote computational resources that are usually locally limited. Scheduling is one of the active research topics in grid environments. The goal of grid task scheduling is to achieve high system throughput and to allocate various computing resources to applications. The Complexity of scheduling problem increases with the size of the grid and becomes highly difficult to solve effectively. Many different methods have been proposed to solve this problem. Some of these methods are based on heuristic techniques that provide an optimal or near optimal solution for large grids. In this paper, a hybrid genetic and particle swarm optimization (PSO) algorithms for scheduling meta-tasks in grid computing system is presented which tries to minimize makespan. According to the experimental results, the proposed algorithm confidently demonstrates its competitiveness with well-known previously proposed algorithms.

Grid Computing, Heuristic, Makespan, ETC Matrix