Accelerating Dynamic Web Content Delivery Using Keyword-Based Fragment Detection

Abstract

AbstractThe recent trend in the Internet traffic is increasing in requests for dynamic and personalized content. To efficiently serve this trend, several server- side and cache-side fragment-based techniques, which exploit reuse of Web pages at the sub-document level, have been proposed. Most of these techniques do not focus on the creation of the fragmented content from existing dynamic content. Also, existing caching techniques do not support fragment movement across the document, a common behavior in dynamic content.This paper presents two proposals that we have suggested to solve these problems. The first, DyCA, a dynamic content adapter, takes original dynamic Web content and converts it to fragment-enabled content. Thus the dynamic parts of the document are separated into separate fragments from the static template of the document. This is dependent on our proposed keyword-based fragment detection approach that uses predefined keywords to find these fragments and to split them out of the core document. Our second proposal, an augmentation to the ESI standard, allows splitting the information of the position of each fragment in the template from the template data itself by using a mapping table. Using this, a fragment enabled cache can have a more fine grained level of identifying fragments independent of their location on the template, which enables it to take into account fragment behaviors such as fragment movement.We used the content taken from three real Web sites to achieve a detailed performance evaluation of our proposals. Our results show that our keyword-based approach for fragment extraction provides us with cacheable fragments that, when combined with our proposed mapping table augmentation, can provide significant advantages for fragment-based Web caching of existing dynamic content.KeywordsMapping TableContent DeliveryDynamic ContentCache ProxyCache BehaviorThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.