Mr. Jorge M. Londoño Profile

Jorge M. Londoño

at Boston Univ

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Proceedings Article | 19 January 2009 Paper

A two-tiered on-line server-side bandwidth reservation framework for the real-time delivery of multiple video streams

Jorge Londoño, Azer Bestavros

Proceedings Volume 7253, 725305 (2009) https://doi.org/10.1117/12.815559

KEYWORDS: Video, Clouds, Computer programming, Statistical multiplexing, Internet, Multiplexing, Video coding, Statistical analysis, Analytical research, Multimedia

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The advent of virtualization and cloud computing technologies necessitates the development of effective mechanisms for the estimation and reservation of resources needed by content providers to deliver large numbers of video-on-demand (VOD) streams through the cloud. Unfortunately, capacity planning for the QoS-constrained delivery of a large number of VOD streams is inherently difficult as VBR encoding schemes exhibit significant bandwidth variability. In this paper, we present a novel resource management scheme to make such allocation decisions using a mixture of per-stream reservations and an aggregate reservation, shared across all streams to accommodate peak demands. The shared reservation provides capacity slack that enables statistical multiplexing of peak rates, while assuring analytically bounded frame-drop probabilities, which can be adjusted by trading off buffer space (and consequently delay) and bandwidth. Our two-tiered bandwidth allocation scheme enables the delivery of any set of streams with less bandwidth (or equivalently with higher link utilization) than state-of-the-art deterministic smoothing approaches. The algorithm underlying our proposed framework uses three per-stream parameters and is linear in the number of servers, making it particularly well suited for use in an on-line setting. We present results from extensive trace-driven simulations, which confirm the efficiency of our scheme especially for small buffer sizes and delay bounds, and which underscore the significant realizable bandwidth savings, typically yielding losses that are an order of magnitude or more below our analytically derived bounds.

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