Abstract
Grids functionally combine globally distributed computers and information systems for creating a universal source of computing power and information. A key characteristic of grids is that resources (e.g., CPU cycles and network capacities) are shared among numerous applications, and therefore, the amount of resources available to any given application highly fluctuates over time. In this paper we analyze the impact of the fluctuations in the processing speed on the performance of grid applications. Extensive lab experiments show that the burstiness in processing speeds has a dramatic impact on the running times, which heightens the need for dynamic load balancing schemes to realize good performance. Our results demonstrate that a simple dynamic load balancing scheme based on forecasts via exponential smoothing is highly effective in reacting to the burstiness in processing speeds.
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References
Banicescu, I., Velusamy, V.: Load balancing highly irregular computations with the adaptive factoring. In: Proceedings of the International Parallel and Distributed Processing Symposium (IPDPS) (2002)
Attiya, H.: Two phase algorithm for load balancing in heterogeneous distributed systems. In: Proceedings of the 12th Euromicro conference on parallel, distributed and network-based processing (2004)
Shirazi, B.A., Hurson, A.R., Kavi, K.M.: Scheduling and Load Balancing in Parallel and Distributed Systems. IEEE CS Press, Los Alamitos (1995)
Zaki, M.J., Li, W., Parthasarathy, S.: Customized dynamic load balancing for a network of workstations. Journal of Parallel and Distributed Computing 43, 156–162 (1997)
Nemeth, Z., Gombas, G., Balaton, Z.: Performance evaluation on grids: Directions, issues and open problems. In: Proceedings of the 12th Euromicro Conference on Parallel, Distributed and Network-based Processing (2004)
Banicescu, I., Liu, Z.: Adaptive factoring: A dynamic scheduling method tuned to the rate of weight changes. In: Proceedings of the High Performance Computing Symposium (HPC), pp. 122–129 (2000)
Cariño, R.L., Banicescu, I.: A load balancing tool for distributed parallel loops. In: International Workshop on Challenges of Large Applications in Distributed Environments, pp. 39–46 (2003)
Evans, D.J.: Parallel SOR iterative methods. Parallel Computing 1, 3–18 (1984)
Hageman, L.A., Young, D.M.: Applied Iterative Methods. Academic Press, London (1981)
Wolski, R., Spring, N.T., Hayes, J.: Predicting the CPU availability of time-shared unix systems on the computational grid. Cluster Computing 3, 293–301 (2000)
Berman, F.D., Wolski, R., Figueira, S., Schopf, J., Shao, G.: Application-level scheduling on distributed heterogeneous networks. In: Proceedings of the 1996 ACM/IEEE conference on Supercomputing, p. 39. ACM Press, New York (1996)
Wolski, R.: Forecasting network performance to support dynamic scheduling using the Network Weather Service. In: HPDC, pp. 316–325 (1997)
Shum, K.H.: Adaptive distributed computing through competition. In: Proceedings of the International Conference on Configurable Distributed Systems, pp. 200–227. IEEE Computer Society, Los Alamitos (1996)
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Dobber, M., Koole, G., van der Mei, R. (2004). Dynamic Load Balancing for a Grid Application. In: Bougé, L., Prasanna, V.K. (eds) High Performance Computing - HiPC 2004. HiPC 2004. Lecture Notes in Computer Science, vol 3296. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30474-6_38
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DOI: https://doi.org/10.1007/978-3-540-30474-6_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24129-4
Online ISBN: 978-3-540-30474-6
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