Abstract
Importance and need of grid process scheduling have been increased in accordance with development of grid computing. In order to distribute and utilize grid processors efficiently, grid computing system needs scheduling policies that manage and schedule grid process. This paper reviews current scheduling policies and proposes an efficient scheduling model which is called the predictive process scheduling model. For efficient scheduling, this paper presents the processing time prediction algorithm to resolve problems of grid scheduling. The predictive process scheduling model predicts processing times of processors, allocates a job to a processor with minimum processing time, and minimizes overall system execution times. For performance evaluation, this paper measures turn-around time, job loss, throughput, and utilization. Empirical results show that the predictive process scheduling model operates with the less 69.5% of turn-around time and the less 76.4% of job loss and improves the more 119.6 % of throughput and the more 117.8% of utilization than those of existing scheduling models such as random scheduling and round-robin scheduling.
This research was supported by the MIC(Ministry of Information and Communication), Korea, under the ITRC(Information Technology Research Center) support program supervised by the IITA(Institute of Information Technology Assessment).
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.
Similar content being viewed by others
References
Berman, F., Fox, G., Hey, T.: Grid computing: making the global infrastructure a reality. J. Wiley, New York (2003)
Foster, I., Kesselman, C.: The Grid: Blueprint for a new Computing Infrastructure. Morgan Kaufmann Publishers, San Francisco (1998)
Krauter, K., Buyya, R., Maheswaran, M.: A taxonomy and survey of grid resource management systems for distributed computing. In: Software Practice and Experience, pp. 135–164 (2002)
Buyya, R., Abramson, D., Giddy, J.: Nimrod/G: an architecture for a resource management and scheduling system in a global computational grid. In: Proceedings of High Performance Computing in the Asia-Pacific Region, vol. 1, pp. 283–289 (2000)
Hamscher, V., Schwiegelshohn, U., Streit, A., Yahyapour, R.: Evaluation of Job-Scheduling Strategies for Grid Computing. In: Buyya, R., Baker, M. (eds.) GRID 2000. LNCS, vol. 1971, pp. 191–202. Springer, Heidelberg (2000)
Dan, M., Wei, Z.: A Static Task Scheduling Algorithm in Grid Computing. In: Li, M., Sun, X.-H., Deng, Q.-n., Ni, J. (eds.) GCC 2003. LNCS, vol. 3033, pp. 153–156. Springer, Heidelberg (2004)
Yuanyuan, Z., Yasushi, I., Hong, S.: A Dynamic Task Scheduling Algorithm for Grid Computing System. In: Cao, J., Yang, L.T., Guo, M., Lau, F. (eds.) ISPA 2004. LNCS, vol. 3358, pp. 578–583. Springer, Heidelberg (2004)
Lichen, Z.: Scheduling Algorithm for Real-Time Applications in Grid Environment. In: 2002 IEEE International Conference, vol. 5 (2002)
Wang, Q., Gui, X., et al.: De-centralized Job Scheduling on Computational Grids Using Distributed Backfilling. In: Jin, H., Pan, Y., Xiao, N., Sun, J. (eds.) GCC 2004. LNCS, vol. 3251, pp. 285–292. Springer, Heidelberg (2004)
Gao, Y., Rong, H., Tong, F., et al.: Adaptive Job Scheduling for a Service Grid Using a Genetic Algorithm. In: Li, M., Sun, X.-H., Deng, Q.-n., Ni, J. (eds.) GCC 2003. LNCS, vol. 3033, pp. 65–72. Springer, Heidelberg (2004)
Zeigler, B.P., et al.: The DEVS Environment for High-Performance Modeling and Simulation. IEEE CS & E 4(3), 61–71 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jang, S.H., Lee, J.S. (2006). Predictive Grid Process Scheduling Model in Computational Grid. In: Shen, H.T., Li, J., Li, M., Ni, J., Wang, W. (eds) Advanced Web and Network Technologies, and Applications. APWeb 2006. Lecture Notes in Computer Science, vol 3842. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11610496_68
Download citation
DOI: https://doi.org/10.1007/11610496_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31158-4
Online ISBN: 978-3-540-32435-5
eBook Packages: Computer ScienceComputer Science (R0)