Abstract
Grid computing has a great potential for grand challenge scientific problems such as Molecular Simulation, High Energy Physics and Genome Informatics. Exploiting under-utilized resources is crucial for a cost-effective, large-scale grid computing platform (i.e., computational grid), but there has been little research work on how to predict what resources will be under-loaded in the near future. In this paper, we analyze idle CPU cycles of PCs at university computer labs and present techniques for predicting idle cycles to be effi-ciently scheduled for parallel/distributed computing. Our experiments with eight month monitoring data show that the accuracy of our prediction techiques is over 85%. Especially, the ratio of critical failure, which predicts that what is actually busy be idle, was only 3.2% out of total subject PCs during the experimental period.
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
R. Golding, P. Bosch, C. Stalin, T. Sullivan, and J. Wilkes. Idleness is not sloth. USENIX Winter Conference, 1995. pg. 201–212.
A. Acharya, G. Edjlali, J. Saltz. The utility of exploiting idle workstations for parallel computation. In ACM SIGMETRICS 1997. pg. 225–235.
M. Mutka and M. Livny. Profiling workstations’ available capacity for remote execution. In Performance’87. pg. 529–544.
M. Samadani and E. Kaltofen. Prediction based task scheduling in distributed computing. In ACM Symposium on the Principles of Distributed Computing, 1995. pg. 261.
M. Harchol-Balter and A. Downey. Exploiting process lifetime distributions for dynamic load balancing. In ACM SIGMETRICS 1996. pg. 13–24.
P. Wyckoff, T. Johnson, and K. Jeong. Finding Idle Periods on Network of Workstations. Technical Report 761, NYU Computer Science.
Litzkow, M., Livny, M. and Mutka, M. Condor — A hunter of idle workstations. Proc. Of 8th International Conference on Distributed Computing Systems, June, 1988.
Liu, C., Yang, L., Foster, I. and Angulo, D. Design and evaluation of a resource selection framework for Grid applications. Proc. Of 11th IEEE Symposium on High Performance Distributed Computing, July, 2002.
Miller, N. and Steenkiste, P. Collecting network status information for network-aware applications. INFOCOM’00, March, 2000
Raman, R., Livny, M. and Solomon, M. Matchmaking: Distributed resource management for high throughput computing. Proc. Of 7th IEEE Symp. On High Performance Distributed Computing, July, 1998.
Subramani, V., Kettimuthu, R. Srinivasan, S. and Sadayappan, P. Distributed job scheduling on computational grids using multiple simultaneous requests. Proc. Of 11th IEEE Symp. On High Performance Distributed Computing, July, 2002.
Foster, I. and Kesselman, C. Globus: A Toolkit-based Grid Architecture. In Foster and Kesselman, C. eds. The Grid: Blueprint for a New Computing Infrastructure, Morgan Kaumann, 1999, 259–278.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hwang, S. et al. (2003). An Analysis of Idle CPU Cycles at University Computer Labs. In: Kumar, V., Gavrilova, M.L., Tan, C.J.K., L’Ecuyer, P. (eds) Computational Science and Its Applications — ICCSA 2003. ICCSA 2003. Lecture Notes in Computer Science, vol 2667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44839-X_77
Download citation
DOI: https://doi.org/10.1007/3-540-44839-X_77
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40155-1
Online ISBN: 978-3-540-44839-6
eBook Packages: Springer Book Archive