Automatic Performance-Optimal Offloading of Network Functions on Programmable Switches
Xiang Chen, Hongyan Liu, Dong Zhang, Zili Meng, Qun Huang, Haifeng Zhou, Chunming Wu, Xuan Liu, Qiang Yang
Abstract
In network function virtualization (NFV), network functions (NFs) are chained as a service function chain (SFC) to enhance NF management with low cost and high flexibility. Recent NFV solutions indicate that the packet processing performance of SFCs can be significantly improved by offloading NFs to programmable switches. However, such offloading requires a deep understanding of heterogeneous NF properties (e.g., NF resource consumption and NF performance behaviors) to achieve the maximum SFC performance. Unfortunately, none of existing solutions provide automatic analysis of these NF properties. Thus, network administrators have to manually examine the source codes of NFs and profile various NF properties by hand, which is extremely time-consuming and laborious. In this article, we propose LightNF, a novel system that simplifies NF offloading in programmable networks. LightNF automatically dissects comprehensive NF properties by means of code analysis and performance profiling while eliminating manual efforts. It then leverages its analysis results of NF properties in its SFC placement so as to make the performance-optimal offloading decisions. We have implemented LightNF on Tofino-based hardware programmable switches. We perform extensive experiments to evaluate LightNF with a real-world testbed and large-scale simulation. Our experiments show that LightNF outperforms existing solutions with an orders-of-magnitude reduction in per-packet processing latency and 9.5× improvement in SFC throughput.