Grid computing is a group of clusters connected over high-speed networks that involves coordinating and sharing computational power, resource utilization is an important issue.That's why some new technique needed to maximize Grid resource utilization. This paper proposes grid level resource scheduling with Job Grouping strategy that maximizes the resource utilization and minimizes processing time of jobs. Also Best Fit followed by Round Robin policy applies at local level to achieve better performance. This method avoids starvation problem of jobs.

Grid computing provides a high performance computing platform to solve larger scale applications by coordinating and sharing computational power, data storage and network resources across dynamic and geographically dispersed organizations. Scheduling onto the Grid is NPcomplete, so there is no best scheduling algorithm for all grid computing systems. An alternative is to select an appropriate scheduling algorithm to use in a given grid environment because of the characteristics of the tasks, machines and network connectivity. Job scheduling is one of the key research area in grid computing. The goal of scheduling is to achieve highest possible system throughput and to match the application need with the available computing resources. Motivation of this study is to encourage and help the amateur researcher in the field of grid computing, so that they can understand easily the concept of scheduling and can contribute in developing more efficient and practical scheduling algorithm. This will benefit interested researchers to carry out further work in this thrust area of research.

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Grid computiug is the ultimate framework that provides a high performance computing environment to meet growing and larger scale computational demands. However, Grid Computing is a critical and complex undertaking as the management of resources and computational jobs are geographically distributed under the ownership of different individuals or organizations with their own access policies, dynamic availability and heterogeneous in nature. Therefore, it is a big challenge and pivotal issue to design an efficient job scheduling algorithm for implementation in the real grid system. Various works has been done by many researchers, still further analysis and research needs to be done to design new techniques and improve the performance of scheduling algorithm in grid computing. The main purpose of this paper is to develop an efficient job scheduling algorithm to maximize the resource utilization and minimize processing time of the jobs. The proposed job scheduling is based on job grouping concept taking into account Memory constraint together with other constraints such as Processing power, Bandwidth, expected execution and transfer time requirements of each job. These very constraints are taken at job level rather than at group level. The experimental results demonstrate that the proposed scheduling algorithm efficiently reduces the processing time of jobs in comparison to others.

Grid computing is a group of clusters connected over high-speed networks that involves coordinating and sharing computational power, data storage and network resources operating across dynamic and geographically dispersed locations. However, scheduling in grid is confronted with many challenges, because resources are heterogeneous, geographically dispersed and dynamic in nature. In this paper, a secure scheduling model is presented, that performs job grouping activity at runtime and the simulation results shows significant improvement in the processing time of jobs and resource utilization as compared to others.