Abstract
The specific problem that underlies in collaborating Grids is scheduling of resources with no knowledge about availability of the resources due to the distributed and autonomous nature of the underlying Grid systems. In this paper, we propose a fully decentralized and probabilistic resource management scheme for Grid systems collaborating based on peer-to-peer communication paradigm. The key idea we employ is to use benchmarked performance measures about the static resource information and calculate the job execution workload. Then this benchmarked job execution time is used to predict the job scheduling feasibility in the face of resource dynamism on the target system. We design our scheme as self adjusting to the actual resource behavior and performance. Simulation results validate the appropriateness of our scheme.
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
Aggarwal, K., Kent, R.D.: An Adaptive Generalized Scheduler for Grid Applications. In: Proc. of the 19th Annual Int’l Symposium on High Performance Computing Systems and Applications (HPCS 2005), Guelph, May 2005, pp. 15–18. Ontario, Canada (2005)
Mateescu, G.: Quality of Service on the Grid via Meta-scheduling with Resource Co-Scheduling and Co-Reservation. Int’l Journal of High Performance Computing Applications 17(3), 209–218 (2003)
Lee, B.-D., Weissman, J.B.: Adaptive Resource Selection for Grid-Enabled Network Services. In: Second IEEE Int’l Symposium on Network Computing and Applications, pp. 75–81 (2003)
Sun, X.-H., Wu, M.: Grid Harvest Service: A System for Long-Term, Application-Level Task Scheduling. In: Int’l Parallel and Distributed Processing Symposium (IPDPS 2003), pp. 25–34 (2003)
Jang, S.-H., Wu, X., Taylor, V., et al.: Using Performance Prediction to Allocate Grid Resources, SC 2004 Posters, Pittsburgh, PA (November 2004)
Daniel, P., Crine, W., Brasileiro, F.: Trading Cycles for Information: Using Replication to Schedule Bag-of-Tasks Applications on Computational Grids. In: Kosch, H., Böszörményi, L., Hellwagner, H. (eds.) Euro-Par 2003. LNCS, vol. 2790. Springer, Heidelberg (2003)
Subramani, V., Kettimuthu, R., Srinivasan, S., Sadayappan, P.: Distributed Job Scheduling on Computational Grids Using Multiple Simultaneous Requests. In: 11th IEEE Int’l Symposium on High Performance Distributed Computing, pp. 359–364 (2002)
Santos-Neto, E., et al.: Exploiting Replication and Data Reuse to Efficiently Schedule Data-Intensive Applications on Grids. In: Proc. of 10th Int’l Workshop, JSSPP, New York, NY (June 2004)
Maheswaran, M., Ali, S., Siegel, H.J., Hensgen, D., Freund, R.F.: Dynamic Matching and Scheduling of a Class of Independent Tasks onto Heterogeneous Computing Systems. J. of Parallel and Distributed Computing 59, 107–131 (1999)
Standard Performance Evaluation Corporation (SPEC), http://www.spec.org/
Messing, F.: Predicting Scheduling Success. In: SpaceOps Symposium, Germany
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(5-6), 757–768 (1999)
Dong, F., Akl, S.G.: Scheduling Algorithms for Grid Computing: State of the Art and Open Problems. Technical Report, School of Computing, Queen’s University, Ontario Canada (January 2006), www.cs.queensu.ca/TechReports/Reports/2006-504.pdf
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rao, I., Huh, EN. (2008). A Probabilistic Approach for Fully Decentralized Resource Management for Grid Systems. In: Vazão, T., Freire, M.M., Chong, I. (eds) Information Networking. Towards Ubiquitous Networking and Services. ICOIN 2007. Lecture Notes in Computer Science, vol 5200. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89524-4_37
Download citation
DOI: https://doi.org/10.1007/978-3-540-89524-4_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89523-7
Online ISBN: 978-3-540-89524-4
eBook Packages: Computer ScienceComputer Science (R0)