Grid computing provides virtual organizations of geographically distributed users with software/hardware infrastructures that allow the effective sharing of computational and storage resources. A Computational Grid offers a set of services that automate the access to resources by exploiting the use of underutilized resources. Resources in Computational Grid are heterogeneous and are owned and managed by other organizations with different access policies. In Computational Grid main emphasis is given on performance in terms of Execution time. This can be accomplished by different Scheduling policies. Scheduling is the decision process by which application components are assigned to available resources to optimize various performance metrics. An important objective of Grid scheduling is improving overall system performance by increasing system throughput, efficiency, and decreasing job completion time. Hence in this paper we have specifically focused on improving Computational grid performance in terms of time. Initially a concept of Scheduling in Computational Grid is discussed in detail. Then we have a talked about the simulator and different Existing Strategies i.e. Space based and Time based in Computational Grid. These strategies are simulated using GridSim Simulator. Based on the analysis result of these two strategies we have proposed a dynamic scheduling algorithm. This algorithm toggles between Space based and Time based strategies. We argue that this will improve the performance by reducing execution time.

1. N.Malarvizhi, Dr V.Rhymend Uthariaraj “A Minimum Time To Release Job Scheduling Algorithm in Computational Grid Environment” 2009 IEEE Fifth International Joint Conference on INC, IMS and IDC.
2. Ian Foster, Carl Kesselman, Steven Tuecke; The Anatomy of the Grid Enabling Scalable Virtual Organizations; Intl J. Supercomputer Applications, 2001.
3. I. Foster, C. Kesselman (eds.). The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, 1999.
4. Technical Report No. 2006-504 “Scheduling Algorithms for Grid Computing: State of the Art and Open Problems” Fangpeng Dong and Selim G. Akl School of Computing, Queen’s University Kingston, Ontario January 2006
5. Jia Yu, Rajkumar Buyya and Kotagiri Ramamohanarao, “Workflow Scheduling Algorithms for Grid Computing”
6. Tao Wang, Xing-she Zhou, Qiu-rang Liu and Yun-lan Wang,An Adaptive Resource Scheduling Algorithm for Computational Grid.In Proceeding of the 2006 IEEE Asia-Specific Conferenceon Services Computing
7. R. Buyya and M. Murshed. GridSim: A Toolkit for the Modeling and Simulation of Distributed Management and Scheduling for Grid Computing. Concurrency and Computation: Practice and Experience (CCPE), 14:13{15, 2002.
8. N.Malarvizhi, Dr V.Rhymend Uthariaraj “A Broker-Based Approach to Resource Discovery and Selection in Grid Environments” 2008 IEEE International Conference on Computer and Electrical Engineering.

Grid Computing Computational Grid Scheduling GridSim etc