JACIII Vol.12 No.5 pp. 479-484
doi: 10.20965/jaciii.2008.p0479


An Evolutionary Hybrid Scheduling Algorithm for Computational Grids

Shajulin Benedict*, Rejitha R. S**, and V. Vasudevan*

*Software Technologies Lab, TIFAC Core in Network Engineering
Sirivilliputhur, India-626190

**Department of Computer Engineering, Kalasalingam University
Sirivilliputhur, India- 626190

December 21, 2007
March 12, 2008
September 20, 2008
grid computing, niching, simulated annealing, scheduling
Grids promote user collaboration through flexible, coordinated sharing of distributed resources to solve a single large problem. Grid scheduling, similar to resource discovery and monitoring, is inherently more complex in Grid environments. We propose two approaches for solving Grid scheduling problems with the simultaneous objectives of maximizing the number of workflow executions and minimizing the waiting time variance among tasks of each workflow. One is the multiple objective Niched Pareto Genetic Algorithm (NPGA) that involves evolution during a comprehensive search and work on multiple solutions. After the Genetic search, we strengthen the search using Simulated Annealing as a local search meta-heuristic. For comparison, we evaluate other scheduling, such as, Tabu Search (TS), Simulated annealing (SA), and Discrete Particle Swarm Optimization (Discrete PSO). Results show that our proposed evolutionary Hybrid scheduling involving NPGA with an SA search, works better than other scheduling in considering workflow execution time within a deadline and waiting time variance in tasks with minimal iterations.
Cite this article as:
S. Benedict, R. S, and V. Vasudevan, “An Evolutionary Hybrid Scheduling Algorithm for Computational Grids,” J. Adv. Comput. Intell. Intell. Inform., Vol.12 No.5, pp. 479-484, 2008.
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