Fast Web Page Allocation On a Server Using Self- Organizing Properties of Neural Networks

This paper presents a novel competitive neural network learning approach to schedule requests to a cluster of Web servers. Traditionally, the scheduling algorithms for distributed systems are not applicable to control Web server clusters because different client domains have different Web traffic characteristics, Web workload is highly variable, and Web requests show a high degree of self-similarity. Using the selforganizing properties of neural networks, we map Web requests to servers through an iterative update rule. This update rule is a gradient descent rule to a corresponding energy function that exploits the selfsimilarity of the Web page requests and includes terms for load balancing. Heuristics to select parameters in the update rule that provide balance between hits and load balancing among servers are presented. Simulations show an order of magnitude improvement over traditional DNS based load-balancing approaches. More specifically, performance of our algorithm ranged between 85% to 98% hit rate compared to a performance range of 2% to 40% hit rate for a Round Robin scheme when simulating real Web traffic. As the traffic increases, our algorithm performs much better than the round robin scheme. A detailed experimental analysis is presented in this paper.