Back close

Building semi-intelligent web cache systems with lightweight machine learning techniques

Publication Type : Journal Article

Publisher : Computers Electrical Engineering

Source : Computers & Electrical Engineering, Elsevier, UK, Volume 39, Number 4, p.1174–1191 (2013)

Campus : Amritapuri

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

Center : Center for Computational Engineering and Networking

Department : Computer Science

Verified : Yes

Year : 2013

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 semi-intelligent web cache systems with lightweight machine learning techniques”, Computers & Electrical Engineering, vol. 39, pp. 1174–1191, 2013

Admissions Apply Now