KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
Solving job shop scheduling problem using a hybrid parallel micro genetic algorithm
Applied Soft Computing
| Hi-index | 0.00 |
Optimizing job scheduling is the major issue in achieving high performance in grid computing systems. The grid workload consists of multiple jobs and the execution precedence constraints can be represented by a Directed Acyclic Graph. Genetic algorithms are useful to resolve large scale combinatorial prediction and optimization problems. In this paper, we represent a Coarse-grained parallel genetic algorithm based grid job scheduling model in which we minimize execution time of jobs and makespan of resources, improve utilization of resources. The analysis shows that the scheduling system using the coarse-grained parallel genetic algorithm can allocate job efficiently and effectively.