Abstract
In this paper the problem of making predictions of incoming tasks response times in a cluster node is focused. These predictions have a significant effect in areas such as dynamic load balancing, scalability analysis or parallel systems modelling. This paper presents two new response time prediction models. The first one is a mixed model based on two widely used models, CPU availability and Round Robin models. The second one, called Response Time Prediction (RTP) model, is a completely new model based on a detailed study of different kinds of tasks and their CPU time consuming. The predictive power of these models is evaluated by running a large set of tests and the predictions obtained with the RTP model exhibit an error of less than 2 % in all these experiments.
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
Bell, G., Gray, J.: What’s next in high-performance computing? Communications of the ACM 45(2), 91–95 (2002)
Pfister, G.F.: In search of clusters: The Ongoing Battle in Lowly Parallel Computing, 2nd edn. Prentice Hall, Englewood Cliffs (1998)
Berman, F.D., et al.: Application-level scheduling on distributed heterogeneous networks. In: Proceedings of the International Conference on Supercomputing (1996)
Spring, N.T., Wolski, R.: Application level scheduling of gene sequence comparison on metacomputers. In: Proceedings of the International Conference on Supercomputing, pp. 141–148 (1998)
Wolski, R., Spring, N., Hayes, J.: Predicting the cpu availability of time-shared unix systems on the computational grid. In: Proceedings of the Eighth International Symposium on High Performance Distributed Computing, pp. 105–112. IEEE, Los Alamitos (1999)
Mehra, P., Wah, B.W.: Automated learning of workload measures for load balancing on a distributed system. In: Proceedings of the International Conference on Parallel Processing. Algorithms and Applications, vol. 3, pp. 263–270 (1993)
Dinda, P.A.: Online prediction of the running time of tasks. In: Proceedings of the 10th IEEE International Symposium on High Performance Distributed Computing, pp. 336–337 (2001)
Dinda, P.A.: A prediction-based real-time scheduling advisor. In: Proceedings of the 16th IEEE International Parallel and Distributed Processing Symposium (2002)
Kunz, T.: The influence of different workload descriptions on a heuristic load balancing scheme. IEEE Transactions on Software Engineering 17(7), 725–730 (1991)
Zhou, S.: A trace-driven simulation study of dynamic load balancing. IEEE Transactions on Software Engineering, 1327–1341 (1988)
Benmohammed-Mahieddine, K., Dew, P.M., Kara, M.: A periodic symmetrically-initiated load balancing algorithm for distributed systems. In: Proceedings of the 14th International Conference on Distributed Computing Systems (1994)
Lee, G.H., Woo, W.D., Yoon, B.N.: An adaptive load balancing algorithm using simple prediction mechanism. In: Proceedings of the Ninth International Workshop on Database and Expert Systems Applications, pp. 496–501 (1998)
Shen, K., Yang, T., Chu, L.: Cluster load balancing for fine-grain network services. In: Proceedings of the International Parallel and Distributed Processing Symposium, pp. 51– 58 (2002)
Ferrari, D., Zhou, S.: An empirical investigation of load indices for load balancing applications. In: Proceedings of the 12th IFIP International Symposium on Computer Performance Modelling, Measurement and Evaluation. Elsevier Science Publishers, Amsterdam (1987)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Beltrán, M., Bosque, J.L. (2004). Predicting the Response Time of a New Task on a Beowulf Cluster. In: Wyrzykowski, R., Dongarra, J., Paprzycki, M., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2003. Lecture Notes in Computer Science, vol 3019. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24669-5_19
Download citation
DOI: https://doi.org/10.1007/978-3-540-24669-5_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21946-0
Online ISBN: 978-3-540-24669-5
eBook Packages: Springer Book Archive