Let's Adapt to Network Change: Towards Energy Saving with Rate Adaptation in SDN
Shahin Vakilinia, Samy Zemmouri, Mohamed Cheriet · Conference on Network and Service Management · 2016
The exponential growth of network users and their communication demands has 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 through providing the monitored realtime traffic rates and the ability of fast re-routing assists to lower link data rates via rate-adaptation technique which reduces considerably the power consumption of 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 Programming (MILP) problem. Then, we present four different computationally efficient algorithms namely greedy first fit, greedy best fit, greedy worst fit and a meta-heuristic Genetic Algorithm (GA) based method to solve the problem for a realistic network topology. Simulation results show that the GA-based method consistently outperforms the three other proposed greedy algorithms.