Scheduling Independent Jobs on Computational Grid using Biogeography Based Optimization Algorithm for Makespan Reduction

Due to the development of information and network technologies, idle computers all over the world can be organized and utilized to enhance the overall computation performance. Grid computing refers to the combination of computer resources from multiple administrative domains used to reach a common goal. Grids offer a way of using the information technology resources optimally inside an organization. As the grid environments facilitate distributed computation, the scheduling of grid jobs has become an important issue. This study introduces a novel approach based on Biogeography Based Optimization algorithm (BBO) for scheduling jobs on computational grid. The proposed approach generates an optimal schedule so as to complete the jobs within a minimum period of time. The performance of the proposed algorithm has been evaluated with Genetic Algorithm (GA), Differential Evolution algorithm (DE), Ant Colony Optimization algorithm (ACO) and Particle Swarm Optimization algorithm (PSO).