Abstract
1 Given the dynamic nature of grid resources, adaptation is required to sustain a predictable level of application performance. A prerequisite of adaptation is the recognition of changing conditions. In this paper we introduce an application signature model and performance contracts to specify expected grid application behavior, and discuss our monitoring infrastructure that detects when actual behavior does not meet expectations. Experimental results are given for several scenarios.
This work was supported in part by the National Science Foundation under grants ASC 97-20202, EIA-997502, and the PACI Computational Science Alliance Cooperative Agreement, and by the Department of Energy under contract W-7405-ENG-36.
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
Browne, S., Dongarra, J., Garner, N., London, K., and Mucci, P. A Scalable Cross-Platform Infrastructure for Application Performance Tuning Using Hardware Counters. In Proceedings of Supercomputing 2000 (2000).
Choi, J., ET AL. ScaLAPACK: A Portable Linear Algebra Library for Distributed Memory Computers—Design Issues and Performance. In Proceedings of Supercomputing 96 (1996).
Fahringer, T. Automatic Performance Prediction of Parallel Programs. Kluwer Academic Publishers, 1996.
Foster, I., and Kesselman, C. Globus: A Metacomputing Infrastructure Toolkit. International Journal of Supercomputer Applications 11, 2 (1997).
Foster, I., and Kesselman, C, Eds. The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann Publishers, 1998.
Foster, I., Roy, A., Sander, V., and Winkler, L. End-to-End Quality of Service for High-End Applications. IEEE Journal on Selected Areas in Communications Special Issue on QoS in the Internet (1999).
Kapadia, N., Fortes, J., and Brodley, C. Predictive application-performance modeling in a computational grid environment. In Proceedings of the Eight IEEE Symposium on High-Performance Distributed Computing (1999).
Kennedy, K., ET AL. Grid Application Development Software. http://hipersoft.cs.rice.edu/grads/.
Mehra, P., ET AL. A Comparison of Two Model-Based Performance-Prediction Techniques for Message-Passing Parallel Programs. In Proceedings of the ACM Conference on Measurement & Modeling of Computer Systems (1994).
Petitet, A., ET AL. Numerical Libraries and The Grid: The GrADS Experiments with ScaLAPACK. Tech. Rep. UT-CS-01-460, University of Tennessee, 2001.
Ribler, R., ET AL. Autopilot: Adaptive Control of Distributed Applications. In Proceedings of the Seventh IEEE Symposium on High-Performance Distributed Computing (1998).
Saavedra-barrera, R. H., Smith, A. J., and Miya, E. Performance prediction by benchmark and machine characterization. IEEE Transactions on Computers 38, 12 (1989).
Wolski, R., Spring, N. T., and Hayes, J. The Network Weather Service: A Distributed Resource Performance Forecasting Service for Metacomputing. The Journal of Future Generation Computing Systems (1999).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vraalsen, F., Aydt, R.A., Mendes, C.L., Reed, D.A. (2001). Performance Contracts: Predicting and Monitoring Grid Application Behavior. In: Lee, C.A. (eds) Grid Computing — GRID 2001. GRID 2001. Lecture Notes in Computer Science, vol 2242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45644-9_15
Download citation
DOI: https://doi.org/10.1007/3-540-45644-9_15
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42949-4
Online ISBN: 978-3-540-45644-5
eBook Packages: Springer Book Archive