Grid computing is a high performance computing environment to solve large-scale computational demands. Computational grids emerged as a next generation computing platform which is a collection of heterogeneous computing resources connected by a network across dynamic and geographically dispersed organizations, to form a distributed high performance computing infrastructure. In computational grid main emphasis is given on resource management and job scheduling. The main goal of scheduling is to maximize the resource utilization and minimize processing time of the jobs. Various research works has been done on job scheduling problem in grid, but still further analysis and research needs to be done to improve the performance of scheduling algorithm in computational grid. In this thesis, an efficient job-grouping based approach has been proposed for fine-grained job scheduling in computational grids. Resources in computational grid are heterogeneous in nature, owned and managed by different organizations with different allocation policies. In our scheduling algorithm jobs are scheduled based on resources computational and communication capabilities. Independent fine-grained jobs are grouped together based on the chosen resources characteristics, to maximize resource utilization and minimize processing time and cost. Job scheduling is a decision process by which application components are assigned to available resources to optimize various performance metrics. Hence in this thesis, we have specially focused on improving computational grid performance in terms of makespan and total computation time. A simulation of proposed approach using GridSim toolkit is conducted. The performance of the algorithm is evaluated using above mentioned performance parameters. A comparison of our proposed approach with other existing fine-grained job scheduling strategies is provided. Experimental results show proposed algorithm performs efficiently in computational grid environment
