A Distributed Energy-Efficient Algorithm in Green Content-Centric Networks

In Content-Centric Networking (CCN), most existing works do not consider energy savings by turning off network devices in CCN. In this paper, we systematically analyze the energy efficiency problem in CCN by turning off the content routers and network links. We formulate the energy consumption issue as a Mixed Integer Linear Programming (MILP) model, and propose a centralized solution via spanning tree heuristic and a fully distributed consensus optimization algorithm via the alternating direction method of multipliers (ADMM) to solve the problem for CCN. By duplicating flow variables, the energy consumption problem decomposes into node specific subproblems with local variables. These variables are iteratively driven into consensus via the ADMM. Simulation results reveal that the proposed distributed algorithm is amenable to energy-efficient implementation, due to smaller amount of local information exchange at each iteration. Moreover, the proposed algorithm can converge to final status in a significantly smaller number of iterations compared to the method based on dual decomposition. In addition, our algorithm scales better to large networks and it does not require intensive fine-tuning of the step size.