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A dynamic predictive framework for e-business workload management
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Bacigalupo, D. A., Turner, J. D., Jarvis, Stephen A., 1970-, Dillenberger, D. N. and Nudd, G. R. (2003) A dynamic predictive framework for e-business workload management. In: 7th World Multiconference on Systemics, Cybernetics and Informatics, Orlando, FL, 27-30 Jul 2003. Published in: Proceedings of the 7th World Multiconference on Systemics, Cybernetics and Informatics (SCI 2003) pp. 390-395.
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Abstract
Distributed e-business application platforms such as the web services framework will require sophisticated workload management infrastructures for the routing of client requests to the most appropriate services. This paper introduces a novel and dynamic predictive framework that uses both analytical performance modelling and historical data to extrapolate a request's predicted performance under varying workloads and conditions on different systems. Such a framework can facilitate a routing algorithm in meeting quality of service-based metric requirements. An e-business benchmark is used as a demonstrator to provide an insight into the techniques used in predicting request response times.
| Item Type: | Conference Item (Paper) |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
| Divisions: | Faculty of Science > Computer Science |
| Journal or Publication Title: | Proceedings of the 7th World Multiconference on Systemics, Cybernetics and Informatics (SCI 2003) |
| ISBN: | 9806560019 |
| Editor: | Callaos, N and DiSciullo, AM and Ohta, T and Liu, TK |
| Official Date: | 2003 |
| Number of Pages: | 6 |
| Page Range: | pp. 390-395 |
| Status: | Peer Reviewed |
| Publication Status: | Published |
| Conference Paper Type: | Paper |
| Title of Event: | 7th World Multiconference on Systemics, Cybernetics and Informatics |
| Type of Event: | Conference |
| Location of Event: | Orlando, FL |
| Date(s) of Event: | 27-30 Jul 2003 |
| URI: | http://wrap.warwick.ac.uk/id/eprint/7656 |
Data sourced from Thomson Reuters' Web of Knowledge
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