Abstract
In this paper, we propose a scalable performance management tool for Wide Area Applications. Our objective is to scalably identify non-random associations between pairs of individual Latency Profiles (iLPs) (i.e., latency distributions experienced by clients when connecting to a server) and exploit them in latency prediction. Our approach utilizes Relevance Networks (RNs) to manage tens of thousands of iLPs. Non-random associations between iLPs can be identified by topology-independent measures such as correlation and mutual information. We demonstrate that these non-random associations do indeed have a significant impact in improving the error of latency prediction.
This research is supported by NSF Grants IIS0219909 and EIA0130422.
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Zadorozhny, V., Raschid, L., Gal, A., Ye, Q., Murthy, H. (2005). Using Non-random Associations for Predicting Latency in WANs. In: Ngu, A.H.H., Kitsuregawa, M., Neuhold, E.J., Chung, JY., Sheng, Q.Z. (eds) Web Information Systems Engineering – WISE 2005. WISE 2005. Lecture Notes in Computer Science, vol 3806. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11581062_48
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DOI: https://doi.org/10.1007/11581062_48
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