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Striking a balance between the costs for Web content providers and the quality of service for Web customers. More efficient content delivery over the Web has become an important element of improving Web performance. Content Delivery Networks (CDNs) have been proposed to maximize bandwidth, improve accessibility, and maintain correctness through content replication [11]. With CDNs, content is distributed to cache servers located close to users, resulting in fast, reliable applications and Web services for the users. More specifically, CDNs maintain multiple Points of Presence (PoP) with clusters of (the so-called surrogate) servers that store copies of identical content, such that users ’ requests are satisfied by the most appropriate site (see the figure here). Typically, a CDN topology involves: • A set of surrogate servers (distributed around the world) that cache the origin servers ’ content; • Routers and network elements that deliver

Abstract: Content Delivery Networks (CDNs) have evolved to overcome the inherent limitations of the Internet in terms of user perceived Quality of Service (QoS) when accessing Web content. A CDN replicates content from the origin server to cache servers, scattered over the globe, in order to deliver content to end-users in a reliable and timely manner from nearby optimal surrogates. Content distribution on the Internet has received considerable research attention. It combines development of high-end computing technologies with highperformance networking infrastructure and distributed replica management techniques. Therefore, our aim is to categorize and analyze the existing CDNs, and to explore the uniqueness, weaknesses, opportunities, and future directions in this field. In this paper, we provide a comprehensive taxonomy with a broad coverage of CDNs in terms of organizational structure, content distribution mechanisms, request redirection techniques, and performance measurement methodologies. We study the existing CDNs in terms of their infrastructure, request-routing mechanisms, content replication techniques, load balancing, and cache management. We also provide an indepth analysis and state-of-the-art survey of CDNs. Finally, we apply the taxonomy to map various CDNs. The mapping of the taxonomy to the CDNs helps in “gap ” analysis in the content networking domain. It also provides a means to identify the present and future development in this field and validates the applicability and

Video-on-demand (VoD) service providers are intensely interested in transport, storage, streaming and caching in content delivery networks. Today’s 5,000-hour library may grow toward the 750,000-hour “Long Tail ” movie and TVseries catalog. We propose a method to calculate how much of a library should be cached. Much previous work focused on theoretical caching concepts, or the dynamics of cache filling and reclamation. Our method explicitly considers the impact of the available video server equipment; we present a VoD design tool comprising a novel cost function, hit ratio estimation and heuristic. 1.

* * Preliminary draft. Please do not cite without contacting authors 1 ** Over the past several years, we have seen the emergence of numerous types of socalled "overlay " networks in the Internet. There are many diverse examples of such overlay networks including the content-delivery-caching networks, implemented by companies like Akamai, the peer-to-peer file sharing networks associated with applications such as BitTorrent, the voice-over-IP services offered via Skype, and various testbed networks such as PlanetLab. These overlay networks enhance or modify the basic functioning of traffic handling within the Internet. Overlays exist in the blurry boundary between what we think of as "the Internet " (a globally interconnected network of IP networks) and the applications that exist on top of the Internet. Overlays also blur the boundaries between the network edges (what we think of as being associated with customer end-nodes) and the network core (what we think of as associated with the services that support the Internet). As such, overlays have important technological and policy implications for the evolution of next generation Internet architecture that historically has been based on the so-called "end-to-end " principle ([SRC84], [BC01]) which relied on a relatively clear demarcation between applications and network services, and edge and core responsibilities. Because of the Internet's growing role as basic infrastructure and increasingly central role in the communications industry, and hence, obvious focus for regulation, changes in Internet architecture have important policy and industry structure implications. For example, from a regulatory perspective, the debate over overlays in Internet-space is analogous to the on-going debate over "layered

Abstract—Content distribution over networks is often achieved by using mirror sites that hold copies of files or portions thereof to avoid congestion and delay issues arising from excessive demands to a single location. Accordingly, there are distributed storage solutions that divide the file into pieces and place copies of the pieces (replication) or coded versions of the pieces (coding) at multiple source nodes. We consider a network which uses network coding for multicasting the file. There is a set of source nodes that contains either subsets or coded versions of the pieces of the file. The cost of a given storage solution is defined as the sum of the storage cost and the cost of the flows required to support the multicast. Our interest is in finding the storage capacities and flows at minimum combined cost. We formulate the corresponding optimization problems by using the theory of information measures. In particular, we show that when there are two source nodes, there is no loss in considering subset sources. For three source nodes, we derive a tight upper bound on the cost gap between the coded and uncoded cases. We also present algorithms for determining the content of the source nodes. Index Terms—Content distribution, information measures, minimum cost, network coding.