Optimizing for Video Storage Networking With Recommender Systems

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Abstract

Driven mainly by its adoption as a new media distribution platform for content providers and its ubiquitous availability for the end user's media production and consumption, the Internet is rapidly reshaping. In particular, the stakeholders in the content distribution market are considering exploiting content delivery networks (CDNs) to play a key enabling role allowing them to become part of related value chains. In this paper we discuss how such CDNs rely on autonomous algorithms to optimally use the storage resources, i.e., reducing bandwidth on feeder links, while providing quality of experience (QoE) to the end user. A model is presented to allow the comparison of algorithms that rely on measuring content popularity versus new recommender based algorithms that base caching decisions on predictions of individual end user behavior. The performance and the dynamics of these algorithmic components are assessed based on a theoretical demand model for a cache deployed at one of the various levels in a tree-based access network, inclusive of a single user cache. This scientific analysis allows to carefully pitch some considerations towards infrastructure network providers that deploy such storage networking. © 2012 Alcatel-Lucent.

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