CCN Energy-Delay Aware Cache Management Using Quantized Hopfield

Abstract

Internet infrastructure is going to be re-designed as a core network layer, shifting from hosts to contents. To this end, content centric networking (CCN) as one of the most effective architectures has been proposed with significant features of in-network caching to open new possibilities for energy efficiency in content dissemination. However in energy-efficient CCN, less popular contents are cached near the origin server, and therefore in delay sensitive applications with less popularity, it leads to dropping delayed chunks, increasing energy waste, and degrading the quality of service (QoS). In the present paper, the energy consumption in CCN while being aware of QoS consideration in terms of imposed delay is minimized. The minimization is performed through integer linear programming by considering most of the energy consuming components. However, since this problem is NP-hard, a quantized Hopfield neural network with an augmented Lagrange multiplier method (MEDCCN-QHN) is proposed to derive the solution. The numerical results show that the MEDCCN-QHN achieves to better delay profile compared to the optimal energy-efficient algorithm, and near-optimal energy consumption. Moreover, the method is fast due to its parallel execution capability.