Our main objective is to executing the job in grids using Grid Service Provider (GSP). GSP have resources to execute the jobs. Our aim to enables the GSPs to organize into VOs reducing the cost of execution and guaranteeing maximum profit for the GSPs. We allocate the task to corresponding resources for processing. If the resource capacity is not suitable for processing the particular job we have to split and executing sequentially and finally merge the result from all resources. Our process based on formation of VO. The next generation of scientific experiments and studies are being carried out by large collaborations of researchers distributed around the world engaged in analysis of huge collections of data generated by scientific instruments. Grid computing has emerged as an enabler for such collaborations as it aids communities in sharing resources to achieve common objectives. Data Grids provide services for accessing, replicating and managing data collections in these collaborations. Applications used in such Grids are distributed data-intensive, that is, they access and process distributed datasets to generate results. These applications need to transparently and efficiently access distributed data and computational resources. This thesis investigates properties of data-intensive computing environments and presents a software framework and algorithms for mapping distributed data-oriented applications to Grid resources. The broker provides the framework to realize scheduling strategies with differing objectives. One of the key aspects of any scheduling strategy is the mapping of jobs to the appropriate resources to meet the objectives.