Abstract
Load balancing has been a key concern for locally distributed multiprocessor systems. The emergence of computational grid extends this problem, such as scalability, heterogeneity of computing resources and considerable communication delay. In this paper, we study the problem of scheduling a large number of CPU-intensive jobs on such systems. The time spent by a job in the system is considered as the main issue that needs to be minimized. The proposed dynamic algorithm of scheduling jobs consists of two policies: Instantaneous Distribution Policy (IDP) and Load Adjustment Policy (LAP). Our algorithm does not address directly the load balancing problem since it is completely unrealistic in such large environments, but we will show that even a non-perfectly load balanced system can behave reasonably well by taking into account the jobs’ time demands. The proposed algorithm is evaluated by a series of simulations.
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., Kesselman, C. (eds.): The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, San Francisco (1999)
El-Ghazawi, T., Gaj, K., Alexandridis, N., Vroman, F., Nguyen, N., Radzikowski, J., Samipagdi, P., Suboh, S.: A Performance Study of Job Management Systems. Concurrency and Computation: Practice and Experience 16(13), 1229–1246 (2004)
Casavant, T.L., Kuhl, J.G.: A taxonomy of scheduling in general-purpose distributed computing systems. IEEE Transactions on Software Engineering 14(2), 141–154 (1988)
Kim, C., Kameda, H.: An algorithm for optimal static load balancing in distributed computer systems. IEEE Trans. Comput. 41(3), 381–384 (1992)
Lin, H.-C., Raghavendra, C.S.: A dynamic load-balancing policy with a central job dispatcher (LBC). IEEE Transactions on Software Engineering 18(2), 145–158 (1992)
Mitzenmacher, M.: The power of Two Choices in Randomized Load Balancing. IEEE Trans. Parallel and Distributed Systems 12(10), 1094–1104 (2001)
Shivaratri, N.G., Krueger, P., Singhal, M.: Load distributing for locally distributed systems. Computer, 33–44 (1992)
Barak, A., La’adan, O.: The MOSIX Multicomputer Operating System for High Performance Cluster Computing. Journal of Future Generation Computer Systems 13(4–5), 361–372 (1998)
Sanders, P.: Analysis of nearest neighbor load balancing algorithms for random loads. Parallel Computing 25(80) (1999)
Xu, C., Lau, F., Monien, B., Luling, R.: Nearest neighbor algorithms for load balancing in parallel computers. Concurrency, Practice and Experience 7, 736 (1995)
Zhou, S.: A trace-driven simulation study of dynamic load balancing. IEEE Transactions on Software Engineering 14(9), 1327–1341 (1988)
Wolski, R., Spring, N., Hayes, J.: The network weather service: A distributed resource performance forecasting service for metacomputing. Journal of Future Generation Computing Systems 15, 757–768 (1999)
Francis, P., Jamin, S., Jin, C., Jin, Y., Raz, D., Shavitt, Y., Zhang, L.: IDMaps: a global internet host distance estimation service. IEEE/ACM Transactions on Networking (TON) 9(5), 525–540 (2001)
Agrawal, A., Casanova, H.: Clustering hosts in P2P and global computing platforms. In: 3rd IEEE/ACM International Symposium on CCGrid 2003, 12-15 May 2003, pp. 367–373 (2003)
Theilmann, W., Rothermel, K.: Dynamic distance maps of the Internet. INFOCOM 2000. In: Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings, March 26-30, vol. 1, pp. 275–284. IEEE, Los Alamitos (2000)
Xian-He, S., Ming, W.: GHS: A performance prediction and task scheduling system for Grid computing. In: IEEE International Parallel and Distributed Processing Symposium, IPDPS 2003 (2003)
Nudd, G.R., Kerbyson, D.J., Papaefstathiou, E., Perry, S.C., Harper, J.S., Wilcox, D.V.: PACE – a toolset for the performance prediction of parallel and distributed systems. Int. J. High Performance Computing Applications 3, 228–251 (2000)
Amir, Y., Awerbuch, B., Barak, A., Borgstrom, S., Keren, A.: An opportunity cost approach for job assignment in a scalable computing cluster. IEEE Transactions on Parallel and Distributed Systems 11(7), 760–768 (2000)
Harchol-Balter, M., Downey, A.: Exploiting Process Lifetime Distributions for Dynamic Load Balancing. In: Proceedings of ACM sigmetrics 1998 Conference on Measurement and Modeling of Computer Systems, May 1997, pp. 115–126 (1997)
Eager, D.L., Lazowska, E.D., Zahorjan, J.: The limited performance benefits of migrating active processes for load sharing. In: Proceedings of the 12th ACM Symposium on Operating Systems Principles, pp. 63–72 (1988)
Zhu, W., Socko, P., Kiepuszewski, B.: Migration impact on load balancing—an experience on amoeba. Operating Systems Review 31(1), 43–53 (1997)
Krueger, P., Shivaratri, N.G.: Adaptive location policies for global scheduling. IEEE Transactions on Software Engineering 20(6), 432–444 (1994)
Eager, D.L., Lazowska, E.D., Zahorjan, J.: A comparison of receiver initiated and sender initiated adaptive load sharing. Performance Evaluation 6, 53–68 (1986)
Kunz, T.: The influence of different workload descriptions on a heuristic load balancing scheme. IEEE Transactions on Software Engineering 17(7), 725–730 (1991)
Thanalapati, T., Dandamudi, S.: An efficient adaptive scheduling scheme for distributed memory multicomputers. IEEE Transactions on Parallel and Distributed Systems 12(7), 758–768 (2001)
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
Lu, K., Subrata, R., Zomaya, A.Y. (2006). Towards Decentralized Load Balancing in a Computational Grid Environment. In: Chung, YC., Moreira, J.E. (eds) Advances in Grid and Pervasive Computing. GPC 2006. Lecture Notes in Computer Science, vol 3947. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11745693_46
Download citation
DOI: https://doi.org/10.1007/11745693_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33809-3
Online ISBN: 978-3-540-33810-9
eBook Packages: Computer ScienceComputer Science (R0)