Performance improvement of web caching in Web 2.0 via knowledge discovery

Abstract

Web 2.0 systems are more unpredictable and customizable than traditional web applications. This causes that performance techniques, such as web caching, limit their improvements. Our study was based on the hypotheses that the use of web caching in Web 2.0 applications, particularly in content aggregation systems, can be improved by adapting the content fragment designs. We proposed to base this adaptation on the analysis of the characterization parameters of the content elements and on the creation of a classification algorithm. This algorithm was deployed with decision trees, created in an off-line knowledge discovery process. We also defined a framework to create and adapt fragments of the web documents to reduce the user-perceived latency in web caches. The experiment results showed that our solution had a remarkable reduction in the user-perceived latency even losses in the cache hit ratios and in the overhead generated on the system, in comparison with other web cache schemes.