Enhancing Genetic Based Scheduling in Computational Grid with Bacterial Foraging Optimization Technique

Mapping of jobs to the resources in grid environment is computed under high degree of heterogeneity of resources due to which heuristic and meta-heuristic approaches are the feasible methods to scheduling in grids. In this paper we enhance the genetic based scheduling in computational grids by a bacterial foraging optimization algorithm in grid environment. This paper analyzes the run-length unit parameter of BFO controls and the exploration of the whole search space and then exploits in the promising areas. The HGS scheduling method is improved in order to minimize the makespan and flow time with Bacterial Forging Optimization algorithm to reduce the cost and complete the process submitted by client. By using this algorithm the scheduler will schedule the job to one of the available resources in which the job can be completed with minimal cost and can further reduce the delay.