sfc2cpu: Operating a Service Function Chain Platform with Neural Combinatorial Optimization
Patrick Krämer, Philip Diederich, Corinna Kramer, Rastin Pries, Wolfgang Kellerer, Andreas Blenk · mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) · 2021
Service Function Chaining realized with a microservice based architecture results in an increased number of computationally cheap Virtual Network Functions (VNFs). Pinning cheap VNFs to dedicated CPU cores can waste resources since not every VNF fully utilizes its core. Thus, cheap VNFs should share CPU cores to improve resource utilization. sfc2cpu learns efficient VNF to core assignments that increase throughput and reduce latency compared to three baseline algorithms. To optimize VNF assignments, sfc2cpu uses game theory combined with Neural Combinatorial optimization in a novel way. Measurements in a real hardware testbed show that sfc2cpu increases throughput by up to 36% and reduces latency by up to 59% compared to Round Robin. We show that sfc2cpu can be incrementally deployed and easily integrated into existing infrastructures.