Memory-Efficient Hypercube Flow Table for Packet Processing on Multi-Cores

Flow-based traffic processing systems have been widely deployed on multi-core systems for performance purpose. However, the current flow table design cannot balance parallel processing performance and memory usage of locks, and is unable to automatically adapt to different network settings. To address these issues, in this paper we propose a Hypercube Flow Table (HFT) based on multi- dimension hash tables. HFT supports different lock granularities to allow multiple cores to process packets in parallel and reduce memory usage by assigning a lock for a group of buckets, instead of one lock per bucket as many current designs. Furthermore, HFT can adapt to various network situations by dynamically adjusting buckets assignment. We have tested HFT on a traffic classification system with two Intel Xeon E5504 processors. The results show that HFT can effectively balance memory usage and parallel processing performance, and further adapt to various network settings by adjusting buckets assignment.