Dynamic Learning Optimization Algorithm for P2P-VoD Systems

Nowadays Video-on-Demand are widely used and number of customers increases, consequently, many network problems must be solved to ensure the quality of service. One of the solutions that has proved its efficiency consists in using Peer-to Peers architecture. However, they raise challenges such as peers resource allocation. Most literature on tackle the problem with optimal static rules found at off-line study of the system. In this paper, we propose to formulate this problem as a dynamic optimization problem, then, solve it using a dynamic learning based optimization algorithm. The obtained results show that using a dynamic resource allocation reduces the rejection rate while enhancing the entropy of the system, in the face of a dynamically changing title demand.