doi: 10.7763/IJCTE.2009.V1.1
A Novel Genetic Algorithm for Static Task Scheduling in Distributed Systems
Abstract
The static task scheduling problem in distributed systems is very important because of optimal usage of available machines and accepted computation time for scheduling algorithm. Solving this problem using the dynamic programming and the back tracking needs much more time. Therefore, there are more attempts to solve it using the heuristic methods. In this paper, a new genetic algorithm, named TDGASA, is presented which its running time depends on the number of tasks in the scheduling problem. Then, the computation time of TDGASA to find a sub-optimal schedule is improved by Simulated Annealing (SA). The results show that the computation time of the proposed algorithm decreases compared to an existing GA-based algorithm, although, the completion time of the final scheduled task in the system decreases a little.
Keywords
- Genetic algorithm
- static task scheduling
- distributed systems
- simulated annealing
- (TDGASA) Task Dependent Genetic Algorithm using Simulated Annealing
How to Cite
Amir Masoud Rahmani and Mojtaba Rezvani, "A Novel Genetic Algorithm for Static Task Scheduling in Distributed Systems," International Journal of Computer Theory and Engineering, vol. 1, no. 1, pp. 1-6, 2009. https://doi.org/10.7763/IJCTE.2009.V1.1
Copyright & License
Copyright © 2009 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).