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Grid Scheduling Using Enhanced Ant Colony Algorithm


Affiliations
1 Department of Computer Science and Engineering, PSG College of Technology, Tamil Nadu, India
2 Akshaya College of Engineering and Technology, Tamil Nadu, India
     

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Grid computing is a high performance computing used to solve larger scale computational demands. Task scheduling is a major issue in grid computing systems. Scheduling of tasks is the NP hard problem. The heuristic approach provides optimal solution for NP hard problems .The ant colony algorithm provides optimal solution. The existing ant colony algorithm takes more time to schedule the tasks. In this paper ant colony algorithm improved by enhancing pheromone updating rule such that it schedules the tasks efficiently and better resource utilization. The simulation results prove that proposed method reduces the execution time of tasks compared to existing ant colony algorithm.

Keywords

Pheromone, Swarm Intelligence, Inertia, Grid Scheduling.
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  • Grid Scheduling Using Enhanced Ant Colony Algorithm

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Authors

P. Mathiyalagan
Department of Computer Science and Engineering, PSG College of Technology, Tamil Nadu, India
U. R. Dhepthie
Department of Computer Science and Engineering, PSG College of Technology, Tamil Nadu, India
S. N. Sivanandam
Akshaya College of Engineering and Technology, Tamil Nadu, India

Abstract


Grid computing is a high performance computing used to solve larger scale computational demands. Task scheduling is a major issue in grid computing systems. Scheduling of tasks is the NP hard problem. The heuristic approach provides optimal solution for NP hard problems .The ant colony algorithm provides optimal solution. The existing ant colony algorithm takes more time to schedule the tasks. In this paper ant colony algorithm improved by enhancing pheromone updating rule such that it schedules the tasks efficiently and better resource utilization. The simulation results prove that proposed method reduces the execution time of tasks compared to existing ant colony algorithm.

Keywords


Pheromone, Swarm Intelligence, Inertia, Grid Scheduling.