LightFlow: Speeding up GPU-based flow switching and facilitating maintenance of flow table

Abstract

Flow-based switching is increasingly important in accordance with the growing demand for in-network processing for cloud applications. Flow switching performance tends to be degraded in proportion to the number of flow entries. To reduce the number of flow entries, they can be aggregated by applying wildcard fields. Meanwhile, the existence of the wildcard entry adversely affects the use of a hash-based lookup on a flow table, and thus a linear search is inherent in flow switching. However, the linear search is currently the primary cause of performance limitation. To date, two flow tables, one for hash-based lookup and the other for a wildcard-enabled linear search, have been used for flow switching. While hash-based table lookup is much faster than linear search, it needs to be manually updated for every exact match entry. Maintaining a hash-based table of all the flow switches is not feasible from a network operator viewpoint. In this paper, LightFlow, a mechanism to accelerate software flow switching processing and relieve the burden of maintaining the flow table is proposed. In LightFlow, two-dimensional parallelization of a linear search is introduced to accelerate lookup of the wildcard-enabled flow entries. It also introduces a mechanism that allows updating of the hash table to be performed automatically based on the result of wildcard-aware table lookup. LightFlow satisfies both the need for fast table lookup and feasibility of flow table management which needs to allow a large number of wildcard entries. Experimental results show that LightFlow can increase the speed of lookup of a wildcard-aware flow table three-fold or more compared to the current GPU-based wildcard search mechanisms.