ParaNF: Enabling Delay-Balanced Network Function Parallelism in NFV

In Network Function Virtualization (NFV), multiple network functions cooperate to provide various network services. To reduce the end-to-end latency through a chain of network functions, research hotspots have turned to complete NF parallelism frameworks. However, several issues remain in them such as the manual dependency analysis on NFs and the excessive parallelism for NFs. Therefore, in this paper, we present ParaNF, an effective delay-balanced NF parallelism framework. ParaNF mainly consists of two logical components. First, the ParaNF orchestrator conducts a dynamic dependency analysis to find out which NFs can be parallelized and then conducts a delay-balanced NF parallelism optimization strategy. Second, the ParaNF infrastructure performs light-weight, dynamic packet copying and merging guided by an efficient label mechanism to support high-performance NF parallelism. We implement a ParaNF prototype with DPDK. Our evaluations show that ParaNF not only realizes the line-speed packet processing, but also achieves significant reduction in latency by up to 47% than the traditional SFC and 35% than OpenBox.