Abstract: Grid computing environment involves all kind of resources namely network, software, data, storage and processing units, evolving towards Global computing to solve a single large problem using Grid scheduling architecture that addresses the interaction between the resource management and data management. In this study, two different approaches have been proposed to solve Grid scheduling problem with the objectives of maximizing the Job Completion Ratio (JCR) and minimizing the lateness. A population based evolutionary algorithm that involves evolution during the search process and a single point local search meta-heuristics that work on a single solution called as hybrid evolutionary algorithm. A Threshold Accepting algorithm (TA) proposed is a single point local search meta-heuristic. Proposed algorithms are evaluated and the experimental results are presented for comparison.
S. Benedict and V. Vasudevan , 2007. Scheduling of Scientific Workflows Using Evolutionary and Threshold Accepting Algorithm for Grids . Asian Journal of Information Technology, 6: 859-865.