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

• Published in: Energies (Basel) 2021-06, Vol.14 (11), p.3161
• Main Authors: Oliveira, Tadeu F., Xavier-de-Souza, Samuel, Silveira, Luiz F.
• Format: Article
• Language: English
• Subjects: Algorithms; Computer centers; control-plane; controller; Controllers; Cores; Data centers; Energy conservation; Energy consumption; Energy efficiency; Interfaces; Internet of Things; Latency; Microprocessors; multicore; Network latency; Parallel processing; Power management; Software; software-defined network; Software-defined networking
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en14113161

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.