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

Web caches are used to address the problem of access delay and network congestion in the Internet. The conventional caching methods are, in general, not efficient in dealing with the problems of web cache admission control and replacement. Intelligent or machine learning-based techniques could be used to enhance the web cache performance. However, such techniques generally suffer from huge computational overheads, making them less effective. This research develops a semi-intelligent approach for web cache admission and replacement using a multinomial web object classifier. The performance of this classifier is assessed through simulation experiments using real trace data, which are compared with Least Recently Used (LRU), Least Frequently Used (LFU) and Greedy Dual Size Frequency (GDSF) schemes. The test results show that a properly trained multinomial logistic regression (MLR) model yields better cache performance in terms of hit ratios and disk space utilization. The performance of this lightweight MLR based classification and caching model is examined in comparison with the heavyweight Artificial Neural Network (ANN) based model and the results are encouraging.