Ημερομηνία: Πέμπτη 20 Μαρτίου, 2008 Ώρα: 11:00-12:30
Τοποθεσία: Aίθουσα Συναντήσεων "Στέλιος Ορφανουδάκης", ΙΤΕ, Ηράκλειο, Κρήτη
Host: Ευάγγελος Μαρκάτος
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 talk will describe a self-adaptive technique under a CDN framework on which outsourced content is identified with no a-priori knowledge of (earlier) request statistics. This is achieved 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. The talk will provide details about the algorithmic identification of these communities, and simulation experiments, which attests that this 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.
Dimitrios Katsaros was born in Thetidio (Farsala), Greece in 1974. He received a BSc and a Ph.D. in Computer Science, both from the Aristotle University of Thessaloniki (AUTH), in 1997 and 2004, respectively. He spent two years (2005-2007) as a postdoc in AUTH; since 2005 he is a contracted lecturer at the Department of Computer and Communication Engineering, at the University of Thessaly, teaching courses on Programming Languages (Java, C++), Mobile and Pervasive Computing, and Web Information Retrieval. His research interests include the Web and Internet, social network analysis, mobile and pervasive computing, mobile ad hoc and wireless sensor networks, and information retrieval.