RECANS: Low-Latency Network Function Chains with Hierarchical State Sharing
Jian Zhao, Shujun Zhuang, Jian Li, Haibing Guan · 2020
In this paper, we present RECANS, a low-latency NFV state management framework that supports elastic scaling of NF chains while meeting the increasingly stringent NFV performance requirements. Its design builds on the insight that an instance may be scaled to a local server or a remote server, and we thus adopt a hierarchical state-sharing approach that exploits shared memory and low-latency network (RDMA) features to realize state sharing in a server and across servers. Globally, all state data are divided into partitions spread across NF servers, and we reorganize the partitions dynamically through state migration implemented with carefully chosen RDMA primitives. Locally, each partition is maintained by a node store, which uses shared memory to share state with NFs in the same server and thus avoids remote accesses. Being aware of NF chaining, RECANS also adopts specially designed flow tables that support chain-wide batch operations. Our evaluation shows that RECANS has a submillisecond peak latency during scaling events, and outperforms traditional hash tables by 4.78 times for normal state access throughput in NF chains.