Prefetching in Content Distribution Networks via Web Communities Identification and Outsourcing
| Autor: | Sidiropoulos, Antonis, Pallis, George C., Katsaros, Dimitrios S., Stamos, Kostas, Vakali, Athena I., Manolopoulos, Yannis |
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| Přispěvatelé: | Pallis, George C. [0000-0003-1815-5468], Vakali, Athena I. [0000-0002-0666-6984] |
| Rok vydání: | 2007 |
| Předmět: |
Web standards
Web server medicine.medical_specialty Synthetic data Statistical methods Web development Web 2.0 Content distribution networks (CDN) Computer Networks and Communications Computer science Servers computer.software_genre Social Semantic Web World Wide Web Server Web page medicine Web navigation Data mining Data Web Web services Internet business.industry Web communities Identification (control systems) Computer simulation Web application security Outsourcing Web data mining Data dissemination techniques on the web Web Accessibility Initiative Web prefetching Internet and web-based Web mining Hardware and Architecture The Internet Web mapping Web service business computer Web modeling Software Content distribution networks |
| Zdroj: | World Wide Web |
| ISSN: | 1573-1413 1386-145X |
| DOI: | 10.1007/s11280-007-0027-8 |
| Popis: | Content distribution networks (CDNs) improve scalability and reliability, by replicating content to the "edge" of the Internet. Apart from the pure networking issues of the CDNs relevant to the establishment of the infrastructure, some very crucial data management issues must be resolved to exploit the full potential of CDNs to reduce the "last mile" latencies. A very important issue is the selection of the content to be prefetched to the CDN servers. All the approaches developed so far, assume the existence of adequate content popularity statistics to drive the prefetch decisions. Such information though, is not always available, or it is extremely volatile, turning such methods problematic. To address this issue, we develop self-adaptive techniques to select the outsourced content in a CDN infrastructure, which requires no apriori knowledge of request statistics. We identify clusters of "correlated" Web pages in a site, called Web site communities, and make these communities the basic outsourcing unit. Through a detailed simulation environment, using both real and synthetic data, we show that the proposed techniques are very robust and effective in reducing the user-perceived latency, performing very close to an unfeasible, off-line policy, which has full knowledge of the content popularity. © 2007 Springer Science+Business Media, LLC. 11 1 39 70 Cited By :31 |
| Databáze: | OpenAIRE |
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