Abstract
An Enterprise Desktop Grid (EDG) is a low cost platform that scavenges idle desktop computers to run Grid applications. Since EDGs use idle computer time, it is important to estimate the expected computer availability. Based on this estimation, a scheduling system is able to select those computers with more expected availability to run applications. As a consequence, an overall performance improvement is achieved. Different techniques have been proposed to predict the computer state for an instant of time, but this information is not enough. A prediction model provides a sequence of computer states for different instants of time. The problem is how to identify computer behavior having as input this sequence of states. We identify the need of providing a architecture to model and evaluate desktop computer behavior. Thus, a scheduling system is able to compare and select resources that run applications faster. Experiments have shown that programs run up to 8 times faster when the scheduler selects a computer suggested by our proposal.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Choi, S., et al.: Characterizing and classifying desktop grid. In: Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid, CCGRID 2007, pp. 743–748. IEEE Computer, Washington (2007)
Brent, C., et al.: Planetlab: an overlay testbed for broad-coverage services. ACM SIGCOMM Computer Communication Review 33, 3–12 (2003)
Dinda, P.: The statistical properties of host load. Sci. Program. 7, 211–229 (1999)
Dinda, P.: Online prediction of the running time of tasks, vol. 5, pp. 225–236. Kluwer Academic Publishers, Hingham (2002)
Dinda, P.: A prediction-based real-time scheduling advisor. In: Proc. International Parallel and Distributed Processing Symposium, IPDPS 2002, Abstracts and CD-ROM, pp. 10–17 (2002)
Dobber, M., Koole, G., van der Mei, R.: Dynamic load balancing experiments in a grid. In: Proceedings of the Fifth IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2005), vol. 2, pp. 1063–1070. IEEE Press, New York (2005)
Edwards, W.: Discovery systems in ubiquitous computing. IEEE Pervasive Computing 5(2), 70–77 (2006)
Kang, W., Grimshaw.: Failure prediction in computational grids. In: 40th Annual Simulation Symposium, ANSS 2007, pp. 275–282 (2007)
Meshkova, E., et al.: A survey on resource discovery mechanisms, peer-to-peer and service discovery frameworks. Computer Networks 52(11), 2097–2128 (2008)
Weka Machine Learning Project. Weka, http://www.cs.waikato.ac.nz
Ramachandran, K., Lutfiyya, H., Perry, M.: Decentralized approach to resource availability prediction using group availability in a p2p desktop grid. Future Generation Computer Systems (2010)
Salzberg, S.: C4.5: Programs for machine learning by Quinlan, R. Morgan Kaufmann. Machine Learning 16, 235–240 (1994)
Trunfio, D., et al.: Peer-to-peer resource discovery in grids: Models and systems. Future Generation Computer Systems 23(7), 864–878 (2007)
Vanthournout, K., Deconinck, G., Belmans, R.: A taxonomy for resource discovery. Personal and Ubiquitous Computing 9, 81–89 (2005)
Witten, I., et al.: Weka: Practical machine learning tools and techniques with java implementations (1999)
Wolski, R.: Experiences with predicting resource performance on-line in computational grid settings. Sigmetrics Perform. Eval. Rev. 30, 41–49 (2003)
Wolski, R., Spring, N., Hayes, J.: The network weather service: a distributed resource performance forecasting service for metacomputing. Future Generation Computer Systems 15(5-6), 757–768 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Salinas, S.A., Garino, C.G., Zunino, A. (2012). An Architecture for Resource Behavior Prediction to Improve Scheduling Systems Performance on Enterprise Desktop Grids. In: Cipolla-Ficarra, F., Veltman, K., Verber, D., Cipolla-Ficarra, M., Kammüller, F. (eds) Advances in New Technologies, Interactive Interfaces and Communicability. ADNTIIC 2011. Lecture Notes in Computer Science, vol 7547. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34010-9_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-34010-9_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34009-3
Online ISBN: 978-3-642-34010-9
eBook Packages: Computer ScienceComputer Science (R0)