Abstract
Web Farms are clustered systems designed to provide high availability and high performance web services. A web farm is a group of replicated HTTP servers that reply web requests forwarded by a single point of access to the service. To deal with this task the point of access executes a load balancing algorithm to distribute web request among the group of servers. The present algorithms provides a short-term dynamic configuration for this operation, but some corrective actions (granting different session priorities or distributed WAN forwarding) cannot be achieved without a long-term estimation of the future web load. On this paper we propose a method to forecast web service work load. Our approach also includes an innovative segmentation method for the web pages using EDAs (estimation of distribution algorithms) and the application of semi-naïve Bayes classifiers to predict future web load several minutes before. All our analysis has been performed using real data from a world-wide academic portal.
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Peña, J.M., Robles, V., Marbán, Ó., Pérez, M.S. (2004). Bayesian Methods to Estimate Future Load in Web Farms. In: Favela, J., Menasalvas, E., Chávez, E. (eds) Advances in Web Intelligence. AWIC 2004. Lecture Notes in Computer Science(), vol 3034. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24681-7_24
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DOI: https://doi.org/10.1007/978-3-540-24681-7_24
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