Catching the Flow with Locality Sensitive Hashing in Programmable Data Planes

Flow-based load balancing exploits the parallelism of network traffic to improve forwarding performance. However, in programmable data planes, the concept of “flow” has been changed, which undermines the premise of hashing-based load balancing. The current network hashing algorithms such as Toeplitz and CRC16 cannot recognize the flow containing different packets, may resulting in forwarding performance degradation. In this study, we introduced an approach based on the locality sensitive hashing to the failure at flow recognition. We proved that bit sampling achieves a higher probability that packets belonging to the same flow are mapped to the same queue than Toeplitz and random algorithm. To guarantee the load balancing performance of bit sampling, we proposed a method for bits selection. The experimental results showed that bit sampling could improve the probability by at least 55% over current network hashing algorithms while maintaining competitive load-balancing performance.