This paper studies a video storage problem that occurs in Video-on-Demand (VOD) networks and in other distributed database systems. Videos should be stored in order to respect various constraints, especially available storage and transmission capacities. We show there exists an algorithm to solve this combinatorial problem through a pricing mechanism and that it converges to a solution under some general conditions. Simulation results with up to 43-node networks and up to 300 videos show that the algorithm is fast.

In this paper, we point out an architecture optimization problem for networks delivering services such as Video-On-Demand or, more precisely, two intertwined problems, i.e., the storage allocation of the videos among the storage nodes of the network and the choice of the network topology. We present and investigate the properties of a genetic algorithm which can handle such problems. This algorithm, as well as a greedy heuristics and simulated annealing, are then used to derive solutions in function of link and node cost parameters in a 36-node network. The results show that genetic algorithms are an effective class of algorithms for such problems, and possibly many other topology optimization problems.