Improving Energy Efficiency on SDN Control-Plane Using Multi-Core Controllers

Abstract

Software-defined networks have become more common in data centers. The programmability of these networks is a great feature that allows innovation to be deployed fast, following the increasing number of new applications. This growth comes with a cost of more processing power and energy consumption. Many researchers have tackled this issue using existing routing techniques to dynamically adjust the network forwarding plane to save energy. On the control-plane, researchers have found algorithms for positioning the controller in a way to reduce the number of used links, thus reducing energy. These strategies reduce energy consumption at the expense of processing power of the controllers. This paper proposes a novel approach to energy efficiency focused on the network’s control-plane, which is complementary to the many already existing data-plane solutions. It takes advantage of the parallel processing capabilities of modern off-the-shelf multicore processors to split the many tasks of the controller among the cores. By dividing the tasks among homogeneous cores, one can lower the frequency of operations, lowering the overall energy consumption while keeping the same quality of service level. We show that a multicore controller can use an off-the-shelf multicore processor to save energy while keeping the level of service. We performed experiments based on standard network measures, namely latency and throughput, and standard energy efficiency metrics for data centers such as the Communication Network Energy Efficiency (CNEE) metric. Higher energy efficiency is achieved by a parallel implementation of the controller and lowering each core’s frequency of operation. In our experiments, we achieved a drop of 28% on processor energy use for a constant throughput scenario when comparing with the single-core approach.

A novel energy-saving method for campus wired and dense WiFi network applying machine learning and idle cycling techniques

“…They run on a managing server (it could be a SDN server executing those algorithms as network apps) that also executes the needed machine learning tools. The consumption used by the server processor (Oliveira et al 2021) to implement our model did not exceed 7 W (normal consumption of a single AP). That is, it was negligible.…”

“…The main inconvenience of those techniques is that the usage of extra devices increases the cost and complexity of the energy savings mechanism. In contrast, we do not use extra devices; we implement our method with standard management software and machine learning software (Oliveira et al 2021).…”

An Design of Software Defined Networks and Possibilities of Network Attacks

“…Efficiency of SDN controllers in the context of packet-in message hybrids Poisson channels has been estimated by Oliveira, Xavier-de-Souza, and Silveira [4]. Using packet sojourn time, they depicted OpenFlow packet forwarding.…”

A Comparison of Ryu and Pox Controllers: A Parallel Implementation

“…The effectiveness of parallel methods in improving network performance has been the subject of several research efforts. With parallel implementation, researchers can apply more processor cores at lower frequencies to more network controllers, closing the performance gap in multi-controller scenarios, as done in the research referenced in [15]. Lowering the operating frequency of each core and implementing controllers in parallel results in greater energy efficiency.…”