Building a semi intelligent web cache with light weight machine learning

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.