Optimized decision algorithm for Information Centric Networks

Information Centric Networks (ICN) enable network, server context and user context-awareness, to achieve an enhanced architecture for the delivery of the multimedia content. The information comes from different sources and serves as input for the decision algorithms for choosing the pertinent configuration such as the best server or the suitable delivery path. Therefore, the relevance of the input information and the efficiency of the decision algorithms are both crucial for the system performance. This paper proposes exploiting the multi-criteria optimization algorithms in the context of the ICN. Based on the approach of the reference level decision, an optimized algorithm is proposed, which considers the impact of different network and server parameters, and dynamically adapts the decision to the current state of the system. The additional contribution of the paper is comprehensive video content consumption simulation model, which represents large scale network. This model was designed to compare effectiveness of decision algorithms proposed for ICN. The presented simulation results prove effectiveness of proposed decision algorithm and suggest its deployment on the future media networks.