Let's adapt to network change: Towards energy saving with rate adaptation in SDN

The exponential growth of network users and their communication demands have led to a tangible increment of energy consumption in network infrastructures. A new networking paradigm called Software-Defined Networking (SDN) recently emerged which simplifies network management by offering programmability of network devices. SDN assists to lower link data rates via rate-adaptation technique which reduces power consumption of the network. The main idea behind this paper is to find a distribution of traffic flows over pre-calculated paths which allow adapting the transmission rate of maximum links into lower states. We first formulate the problem as a Mixed Integer Linear Program (MILP) problem. We then present four different computationally efficient algorithms namely greedy first fit, greedy best fit, greedy worst fit and a meta-heuristic genetic algorithm to solve the problem for a realistic network topology. Simulation results show that the genetic algorithm consistently outperforms the three greedy algorithms.