Issue No. 01 - January (2009 vol. 21)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2008.92
Dimitrios Katsaros , University of Thessaly, Volos, Aristotle University of Thessaloniki, Thessaloniki
George Pallis , Aristotle University of Thessaloniki, Thessaloniki
Konstantinos Stamos , Aristotle University of Thessaloniki, Thessaloniki
Athena Vakali , Aristotle University of Thessaloniki, Thessaloniki
Antonis Sidiropoulos , Aristotle University of Thessaloniki, Thessaloniki
Yannis Manolopoulos , Aristotle University of Thessaloniki, Thessaloniki
Content Distribution Networks (CDNs) balance costs and quality in services related to content delivery. Devising an efficient content outsourcing policy is crucial since, based on such policies, CDN providers can provide client-tailored content, improve performance, and result in significant economical gains. Earlier content outsourcing approaches may often prove ineffective since they drive prefetching decisions by assuming knowledge of content popularity statistics, which are not always available and are extremely volatile. This work addresses this issue, by proposing a novel self-adaptive technique under a CDN framework on which outsourced content is identified with no a-priori knowledge of (earlier) request statistics. This is employed by using a structure-based approach identifying coherent clusters of "correlated" Web server content objects, the so-called Web page communities. These communities are the core outsourcing unit and in this paper a detailed simulation experimentation has shown that the proposed technique is robust and effective in reducing user-perceived latency as compared with competing approaches, i.e., two communities-based approaches, Web caching, and non-CDN.
Communication/Networking and Information Technology, Information Search and Retrieval, Information Storage, Systems and Software
A. Vakali, D. Katsaros, K. Stamos, Y. Manolopoulos, A. Sidiropoulos and G. Pallis, "CDNs Content Outsourcing via Generalized Communities," in IEEE Transactions on Knowledge & Data Engineering, vol. 21, no. , pp. 137-151, 2008.