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Grid computing is a high performance computing environment to solve large-scale computational demands. Computational grids has 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. Our work is mainly based on job-grouping approach 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. The performance of the algorithm is evaluated based on above mentioned performance parameters and compared with other existing fine-grained job scheduling strategies using GridSim toolkit.