A Software-Defined Network (SDN) renders flexible traffic engineering, but consumes a lot of energy. There is an overhead on the control-plane because flow rule updates are always performed and there is energy consumption by the forwarding hardware. Current energy-aware SDN methods mostly focus on Static or Greedy optimizations. This can cause too many Ternary Content-Addressable Memory (TCAM) updates and unstable rule churn when traffic changes over time. This article introduces a Dynamic Flow Rule Placement (DFRP) framework for real-time energy optimization in SDN. It reduces network energy usage, TCAM update costs, and rule churn all at the same time. The suggested framework uses a convex relaxation method to take decisions on binary switches, links, and rule placement. It also uses a minimum-edit round scheme that only allows small rule changes between time slots. To further reduce instability in the control plane, batch scheduling and receding horizon optimization (RHO) techniques are used. The system uses predicted traffic for future time slots to make decisions, but only the actions for the current time slot are executed. The experiments are carried out on two real-world dynamic SNDlib topologies such as Germany50 and Nobel-Germany, using 288 five-minute traffic matrices over a one-day period. Comparative results against Static and Greedy baselines show that DFRP saves approx. 30% energy while cutting down on TCAM update overhead and rule churn by approx. 20%, consistently across both the networks. Hence DFRP can be applied on dynamic traffic large-scale networks for stable and energy-efficient SDN operations.
Behera et al. (Fri,) studied this question.