We analyze a peer-assisted Video-on-Demand (VoD) system in which users contribute their upload bandwidth to the redistribution of a video that they are downloading or that they have cached locally. Our target is to characterize the additional bandwidth that servers must supply to immediately satisfy all requests to watch a given video. We develop an approximate fluid model to compute the required server bandwidth in the sequential delivery case, as well as in controlled nonsequential swarms. Our approach is able to capture several stochastic effects related to peer churn, upload bandwidth heterogeneity, and nonstationary traffic conditions, which have not been documented or analyzed before. Finally, we provide important hints for the design of efficient peer-assisted VoD systems under server capacity constraints.
Peer-assisted VoD Systems: An Efficient Modeling Framework / Ciullo, Delia; Martina, Valentina; Garetto, M.; Leonardi, Emilio; Torrisi, G.. - In: IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS. - ISSN 1045-9219. - STAMPA. - 25:7(2014), pp. 1852-1863. [10.1109/TPDS.2013.300]
Peer-assisted VoD Systems: An Efficient Modeling Framework
CIULLO, DELIA;MARTINA, VALENTINA;LEONARDI, Emilio;
2014
Abstract
We analyze a peer-assisted Video-on-Demand (VoD) system in which users contribute their upload bandwidth to the redistribution of a video that they are downloading or that they have cached locally. Our target is to characterize the additional bandwidth that servers must supply to immediately satisfy all requests to watch a given video. We develop an approximate fluid model to compute the required server bandwidth in the sequential delivery case, as well as in controlled nonsequential swarms. Our approach is able to capture several stochastic effects related to peer churn, upload bandwidth heterogeneity, and nonstationary traffic conditions, which have not been documented or analyzed before. Finally, we provide important hints for the design of efficient peer-assisted VoD systems under server capacity constraints.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2538487
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