Storage Allocation Based on Client Preferences by

Video on demand (VoD) systems allow clients to select and watch video content at their desired time. The system consists of a set of video files (movies) and several servers (disks), each having a limited storage capacity, C, and a limited bandwidth (load capacity), L. The goal in the storage allocation problem is to assign the video files to the servers and the bandwidth to the clients in a way that maximizes the utilization of the system. This problem was studied in the past assuming each client provides a single request for a single movie. This paper considers a more general and realistic model – in which each client ranks all the movies in the system. Specifically, for each client j and movie i, it is known how much client j is willing to pay in order to watch movie i. The goal is to maximize the system’s profit. We prove that the problem is NP-complete and present approximation algorithms and heuristics for systems with a single or multiple disks. For a single disk we present an (1−1/e)approximation algorithm that we extended for systems with storage costs, and for k-round broadcasting, in which each client might be serviced multiple times. For multiple disks we present a (C − 1)(e − 1)/Ce-approximation algorithm, two heuristics for determining the storage allocation, and an optimal bandwidth-allocation algorithm. We are also explore a variant of the problem where client provides an ordered tie-free list of movies preferences. In this variant we present two different objectives. One is to find a rankmaximal lexicographic profile. And The second is Fairness. We present hardness proofs for both objectives. For the fairness problem we also show the tight bound of M − C + 1-fair for packing C movies. In our simulation of a VoD system, we compared the performance of the suggested heuristics for systems with variable parameters and clients with variable preference distributions. We focused on systems in which client preferences and payment are power-law distributed: a few movies are very popular and clients are willing to pay significantly more for watching them. Our results can be applied to other subset selection problems in which clients provide preferences over the elements.