A Learning-Based Approach for Web Cache Management
Web caching has been widely used to alleviate Internet traffic congestion in World Wide Web (WWW) services. To reduce download throughput, an effective strategy on web cache management is needed to exploit web usage information in order to make a decision on evicting the document stored in case of cache saturation. This paper presents a so-called Learning Based Replacement algorithm (LBR), a hybrid approach towards an efficient replacement model for web caching by incorporating a machine learning technique (naive Bayes) into the LRU replacement method to improve prediction of possibility that an existing page will be revised by a succeeding request, from access history in a web log. The learned knowledge includes information on which URL objects in cache should be kept or evicted. The learning-based model is acquired to represent the hidden aspect of user request pattern for predicting the re-reference possibility. By a number of experiments, the LBR gains potential improvement of prediction on revisit probability, hit rate and byte hit rate overtraditional methods; LRU, LFU, and GDSF, respectively.
web caching machine learning lru lfu gdsf replacement strategy