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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