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Building a semi intelligent web cache with light weight machine learning

Publication Type : Conference Paper

Publisher : IEEE

Source : Intelligent Systems (IS), 2010 5th IEEE International Conference, IEEE (2010)

Campus : Amritapuri

School : Department of Computer Science and Engineering, School of Engineering

Department : Computer Science

Year : 2010

Abstract : This paper proposes a novel admission and replacement technique for web caching, which utilizes the multinomial logistic regression (MLR) as classifier. The MLR model is trained for classifying the web cache's object worthiness. The parameter object worthiness is a polytomous (discrete) variable which depends on the traffic and the object properties. Using worthiness as a key, an adaptive caching model is proposed. Trace driven simulations are used to evaluate the performance of the scheme. Test results show that a properly trained MLR model yields good cache performance in terms of hit ratios and disk space utilization, making the proposed scheme as a viable semi intelligent caching scheme.

Cite this Research Publication :
Dr. Sajeev G. P. and Sebastian, M. P., “Building a semi intelligent web cache with light weight machine learning”, in Intelligent Systems (IS), 2010 5th IEEE International Conference, 2010

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