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<p><b>Abstract</b>—In designing cost-effective video-on-demand (VOD) servers, efficient resource management and proper system sizing are of great importance. In addition to large storage and I/O bandwidth requirements, support of interactive VCR functionality imposes additional resource requirements on the VOD system in terms of storage space, as well as disk and network bandwidth. Previous works have used data sharing techniques (such as batching, buffering, and adaptive piggybacking) to reduce the I/O demand on the storage server. However, such data sharing techniques complicate the provision of VCR functions and diminish the amount of benefit that can be obtained from data sharing techniques. The <it>main</it> contribution of this paper is a simple, yet powerful, <it>analytical modeling approach</it> which allows for analysis, system sizing, resource allocation, and parameter setting for a fairly general class of data sharing techniques which are used in conjunction with the providing of VCR-type functionality. Using this mathematical model, we can determine the proper amount of resources to be allocated for normal playback as well as for service of VCR functionality requests while satisfying predefined system performance requirements. To illustrate the usefulness of our model, we focus on a specific data sharing scheme which combines the use of batching, buffering, and adaptive piggybacking, as well as allows for the use of VCR functions. We show how to utilize this mathematical model for system sizing and resource allocation purposes—that is, how to distribute the available resources between the service of normal playback and VCR functionality requests under various workloads and resource price ratios, so as to obtain the lowest system cost.</p>
Video-on-demand, data sharing techniques, resource allocation, system sizing

M. Leung, J. Lui and L. Golubchik, "Use of Analytical Performance Models for System Sizing and Resource Allocation in Interactive Video-on-Demand Systems Employing Data Sharing Techniques," in IEEE Transactions on Knowledge & Data Engineering, vol. 14, no. , pp. 615-637, 2002.
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