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 selfadaptive 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

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Abstract-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.

Users tend to use the Internet for "resource-hungry" applications (which involve content such as video, audio on-demand and distributed data) and at the same time, more and more applications (such as e-commerce, elearning etc.)

Content Delivery Networks (CDNs) balance costs and quality in services related to content delivery. This has urged many Web entrepreneurs to make contracts with CDNs. In the literature, a wide range of techniques has been developed, implemented and standardized for improving the performance of CDNs. The ultimate goal of all the approaches is to improve the utility of CDN surrogate servers. In this paper we define a metric which measures the utility of CDN surrogate servers, called CDN utility. This metric captures the traffic activity in a CDN, expressing the usefulness of surrogate servers in terms of data circulation in the network. Through an extensive simulation testbed, we identify the parameters that affect the CDN utility in such infrastructures. We evaluate the utility of surrogate servers under various parameters and provide insightful comments.