Accelerating SDN control plane with GPGPU-based packet classification
Martin Balaz, Pavol Helebrandt · 2019
The increasing number of network traffic and packet processing in Software Defined Networks can be a challenge for the slower handling performance by control plane in software. Parallel processing of network traffic offers a solution to increase the performance of software-based control planes. Integrating large amounts of additional general-purpose CPU cores is relatively costly for achieving a substantial boost in packet classification by parallel processing. In contrast, simplified compute cores in the GPUs of today deliver better performance in execution of massively parallel tasks and offer better scalability. The motivation of this work is to utilize the performance of the GPU computational power for acceleration of control plane. The focus of this paper is packet classification in parallel processing on GPU by applying the principles of parallel programming into existing algorithms.