Decentralized view prediction for global content placement

Abstract : A large portion of today's Internet traffic originates from streaming and video services. Storing, indexing, and serving these videos is a daily engineering challenge that requires increasing amounts of efforts and infrastructures. One promising direction to improve video services consists in predicting at upload time where and when a new video might be viewed, thereby optimizing placement and caching decisions. Implementing such a prediction service in a scalable manner poses significant technical challenges. In this paper, we address these challenges in the context of a decentralized storage system consisting of set-top boxes or end nodes. Specifically, we propose a novel data placement algorithm that exploits information about the tags associated with existing content, such as videos, and uses it to infer the number of views that newly uploaded content will have in each country.
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Communication dans un congrès
Middleware 2015 : ARM Workshop, Dec 2015, Vancouver, Canada. ARM 2015: Proceedings of the 14th International Workshop on Adaptive and Reflective Middleware, 〈10.1145/2834965.2834974〉
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https://hal.inria.fr/hal-01247159
Contributeur : Stephane Delbruel <>
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Distributed under a Creative Commons Paternité - Pas d'utilisation commerciale 4.0 International License

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Stéphane Delbruel, Davide Frey, François Taïani. Decentralized view prediction for global content placement. Middleware 2015 : ARM Workshop, Dec 2015, Vancouver, Canada. ARM 2015: Proceedings of the 14th International Workshop on Adaptive and Reflective Middleware, 〈10.1145/2834965.2834974〉. 〈hal-01247159〉

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