Enhanced dynamic web page allocation using fuzzy neural network

Authors:
Y. K. Liu;L. M. Cheng;L. L. Cheng
Affiliations:
Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR, Hong Kong;Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR, Hong Kong;Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR, Hong Kong
Venue:
AIKED'06 Proceedings of the 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases
Year:
2006

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Abstract

Due to the limited cache size in each server, the traditional neural network techniques applied to improve the cache hit rate of scheduling algorithm in load-balancing web server cannot provide a good performance real web site because it cannot balance the server workload properly. Here, we propose a fuzzy neural network technique by feeding back the real-time system usage with an updating mapping rules based on different requested objects categorized into different servers groups with different cache size and according to their input frequency to enhance the cache hitting rate of scheduling, simulation result shows that the proposed technique keeps 92% to 99% cache hit rate and in parallel finely balances backend server resource usage.