Abstract
We address the problem of how many workers should be allocated for executing a distributed application that follows the master-worker paradigm, and how to assign tasks to workers in order to maximize resource efficiency and minimize application execution time. We propose a simple but effective scheduling strategy that dynamically measures the execution times of tasks and uses this information to dynamically adjust the number of workers to achieve a desirable efficiency, minimizing the impact in loss of speedup. The scheduling strategy has been implemented using an extended version of MW, a runtime library that allows quick and easy development of master-worker computations on a computational grid. We report on an initial set of experiments that we have conducted on a Condor pool using our extended version of MW to evaluate the effectiveness of the scheduling strategy.
This work was supported by the CICYT (contract TIC98-0433) and by the Commission for Cultural, Educational and Scientific Exchange between the USA and Spain (project 99186).
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
I. Foster and C. Kesselman, “The Grid: Blueprint for a New Computing Infraestructure”, Morgan-Kaufmann, 1999.
H. Casanova and J. Dongarra, “NetSolve: Network enabled solvers”, IEEE Computational Science and Engineering, 5(3) pp. 57–67, 1998.
D. Abramson, J. Giddy, and L. Kotler, “High Performance Parametric Modeling with Nimrod/G: Killer Application for the Global Grid?”, in Proc. of IPPS/SPDP’2000, 2000.
J.-P. Goux, S. Kulkarni, J. Linderoth, M. Yoder, “An enabling framework for masterworker applications on the computational grid”, Tech. Report, University of Wisconsin-Madison, March, 2000.
L. M. Silva and R. Buyya, “Parallel programming models and paradigms”, in R. Buyya (ed.), “High Performance Cluster Computing: Architectures and Systems: Volume 2”, Prentice Hall PTR, NJ, USA, 1999.
F. Berman, R. Wolski, S. Figueira, J. Schopf and G. Shao, “Application-Level Scheduling on Distributed Heterogeneous Networks”, Proc. of Supercomputing’96.
H. Casanova, M. Kim, J. S. Plank and J. Dongarra, “Adaptive scheduling for task farming with Grid middleware”, International Journal of Supercomputer Applications and High-Performance Computing, pp. 231–240, Volume 13, Number 3, Fall 1999.
G. Shao, R. Wolski and F. Berman, “Performance effects of scheduling strategies for Master/Slave distributed applications”, Technical Report TR-CS98-598, University of California, San Diego, September 1998.
R. Wolski, N. T. Spring and J. Hayes, “The Network Weather Service: a distributed resource performance forecasting service for metacomputing”, Journal of Future Generation Computing Systems, Vol. 15, October, 1999.
T. B. Brecht and K. Guha, “Using parallel program characteristics in dynamic processor allocation policies”, Performance Evaluation, Vol. 27 and 28, pp. 519–539, 1996.
T. D. Nguyen, R. Vaswani and J. Zahorjan, “Maximizing speedup through self-tuning of processor allocation”, in Proc. of the Int. Par. Proces. Symp. (IPPS’96), 1996.
V. Govindan and M. Franklin, “Application Load Imbalance on Parallel Processors”, in Proc. of the Int. Paral. Proc. Symposium (IPPS’96), 1996.
E. Cantu-Paz, “Designing efficient master-slave parallel genetic algorithms”, in J. Koza, W. Banzhaf, K. Chellapilla, K. Deb, M. Dorigo, D. Fogel, M. Garzon D. E. Goldberg, H. Iba and R. Riolo, editors, Genetic Programming: Proceeding of the Third Annual Conference, San Francisco, Morgan Kaufmann, 1998.
J. Basney, B. Raman and M. Livny, “High throughput Monte Carlo”, Proceedings of the Ninth SIAM Conference on Parallel Processing for Scientific Computing, San Antonio Texas, 1999.
J. Pruyne and M. Livny, “Interfacing Condor and PVM to harness the cycles of workstation clusters”, Journal on Future Generations of Computer Systems, Vol. 12, 1996.
L. A. Hall, “Aproximation algorithms for scheduling”, in Dorit S. Hochbaum (ed.), “Approximation algorithms for NP-hard problems”, PWS Publishing Company, 1997.
D. L. Eager, J. Zahorjan and E. D. Lazowska, “Speedup versus efficiency in parallel systems”, IEEE Transactions on Computers, vol. 38, pp. 408–423, 1989.
E. Heymann, M. Senar, E. Luque, M. Livny. “Evaluation of an Adaptive Scheduling Strategy for Master-Worker Applications on Clusters of Workstations”. Proceedings of 7th Int. Conf. on High Performance Computing (HiPC’2000) (to appear).
M. Livny, J. Basney, R. Raman and T. Tannenbaum, “Mechanisms for high throughput computing”, SPEEDUP, 11, 1997.
A. Geist, A. Beguelin, J. Dongarra, W. Jiang, R. Manchek and V. Sunderam, “PVM: Parallel Virtual Machine A User’s Guide and Tutorial for Networked Parallel Computing”, MIT Press, 1994.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Heymann, E., Senar, M.A., Luque, E., Livny, M. (2000). Adaptive Scheduling for Master-Worker Applications on the Computational Grid. In: Buyya, R., Baker, M. (eds) Grid Computing — GRID 2000. GRID 2000. Lecture Notes in Computer Science, vol 1971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44444-0_20
Download citation
DOI: https://doi.org/10.1007/3-540-44444-0_20
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41403-2
Online ISBN: 978-3-540-44444-2
eBook Packages: Springer Book Archive