Abstract
Computational grids have the potential for solving large-scale scientific problems using heterogeneous and geographically distributed resources. However, a number of major technical hurdles must overcome before this potential can be realized. One problem that is critical to effective utilization of computational grids is the efficient scheduling of jobs. This work addresses this problem by describing and evaluating a grid scheduling architecture and a job-scheduling algorithm. The architecture is scalable and does not assume control of local site resources. In our algorithm Grid Resource Manager or Grid Scheduler performs resource brokering and job scheduling. The scheduler selects computational resources based on job requirements, job characteristics and information provided by the resources. The main aim of these schedulers is to minimize the total time to release for the individual application. The Time To Release (TTR) includes the processing time of the program, waiting time in the queue, transfer of input and output data to and from the resource. Since grid resources are heterogeneous and distributed over many areas the transmission time is very important criteria. In this paper,an algorithm for minimum time to release is proposed. The proposed scheduling algorithm has been compared with other scheduling schemes such as First Come First Served (FCFS) and Min-Min. These existing algorithms does not consider the transmission time (in time and out time) when scheduling jobs to resources. The proposed algorithm has been verified through the GridSim simulation toolkit and the simulation results confirm that the proposed algorithm produce schedules where the execution time of the application is minimized. The average weighted response times of all submitted jobs decrease up to about 19.79%. The results have been verified using different workloads and Grid configurations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Foster, I., Kesseleman, C., Tuecke, S.: The Anatomy of the Grid: Enabling Scalable virtual Organizations. International Journal of Super Computer Applications 15(3) (2001)
Foster, I., Iamnitchi, A.: On Death, Taxes, and the Convergence of Peer-to-Peer and Grid Computing. In: Kaashoek, M.F., Stoica, I. (eds.) IPTPS 2003. LNCS, vol. 2735. Springer, Heidelberg (2003)
Munter 2005, Grid computintg whitepaper (2005)
SETI@homeHomePage, http://setiathome.ssl.berkely.edu
Legion: A world wide virtual computer. University of Virginia (2007), http://legion.virginia.edu/
Seymour, K., Yarkhan, A., Agrawal, S., Dongarra, J.: Netsole: Grid enabling scientific computing environments. In: Grandinetti, L. (ed.) Grid Computing and New Frontiers of High Performance Processing. Elseveir, Amsterdam (2005)
Buyya, R., Abramson, D., Giddy, J.: Nimrod/G: An architecture for a resource management and Scheduling System in a Global Computational Grid (2002)
Coddington, P.: DISCWorld, virtual data grids and grid applications (2002), http://www.gridbus.org/ozgrid/DiscWorld.ppt/
Berman, F., Wolski, R., Casanova, H., Cirne, W., Dail, H., Faerman, M., Figueira, S., Hayes, J., Obertelli, G., Schopf, J., Shao, G., Smallen, S., Spring, N., Su, A., Zagorodnov, D.: Adaptive Computing on the Grid Using AppLeS. IEEE Transactions on Parallel and Distributed Systems 14(4) (April 2003)
Malarvizhi, N., Rhymend Uthariaraj, V.: A Broker-Based Approach to Resource Discovery and Selection in Grid Environments. In: IEEE International Conference on Computer and Electrical Engineering, pp. 322–326 (2008)
Feitelson, D.G., Rudolph, R.: Parallel Job Scheduling: Issues and Approaches. In: Feitelson, D.G., Rudolph, L. (eds.) IPPS-WS 1995 and JSSPP 1995. LNCS, vol. 949, pp. 1–18. Springer, Heidelberg (1995)
Harchol-Balter, M., Leighton, T., Lewin, D.: Resource Discovery in Distributed Networks. In: 18th ACM Symposium on Principles of Distributed Computing (1999)
Benoit, A., Cole, M., Gilmore, S., Hillston, J.: Enhancing the effetive utilization of Grid clusters by exploiting on-line performability analysis. In: 2005 IEEE International symposium on Cluster Computing and the Grid, May 9-12, pp. 317–324 (2005)
Berman, F., Wolski, R., Casanova, H.: Adaptive computing on the Grid using APPLES. IEEE Transactions on Parallel and Distributed Systems 14(4), 369–382 (2003)
Ding, S.-L., Yuan, J.-B., Ju, J.-U.-B.: An algorithm for agent based task scheduling in grid environments. In: Proceedings of 2004 International Conference on Machine Learning and Cybernetics, pp. 2809–2814 (2004)
He, X., Sun, X., Laszewski, G.V.: QoS guided min-min heuristic for grid task scheduling. Journal of Computer Science and Technology 18, 442–451 (2003)
Angulo, D., Foster, I., Liu, C., Yang, L.: Design and evaluation of a resource selection framework for grid applications. In: Proceedings of IEEE International Symposium on High Performance Distributed Computing (2002)
Min, R., Maheswaran, M.: Scheduling co-reservations with priorities in grid computing system. In: Proceedings of 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid, CCGRID 2002 (2002)
Herroelen, W., De Reyck, B., Demeulemeester, E.: Resource constrained project scheduling: A survey of recent developments. Computers and Operations Research 25, 279–302 (1998)
Mokotoff, E.: Parallel machine scheduling problems: A survey. Asia- pacific Journal of Operational Research 18, 193–242 (2001)
Jain, R.: A Survey of Scheduling Methods. Nokia Research Center (September 1997)
Hawa, M.: Stochastic Evaluation of Fair Scheduling with Applications to Quality-of- Service in Broadband Wireless Access Networks. PhD dissertation, Univ. of Kansas (August 2003)
Buyya, R.: A Grid simulation toolkit for resource modeling and application scheduling for parallel and distributed computing, http://www.buyya.com/gridsim/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Malarvizhi, N., Uthariaraj, V.R. (2009). A New Mechanism for Job Scheduling in Computational Grid Network Environments. In: Liu, J., Wu, J., Yao, Y., Nishida, T. (eds) Active Media Technology. AMT 2009. Lecture Notes in Computer Science, vol 5820. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04875-3_50
Download citation
DOI: https://doi.org/10.1007/978-3-642-04875-3_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04874-6
Online ISBN: 978-3-642-04875-3
eBook Packages: Computer ScienceComputer Science (R0)