Packet classification using coarse-grained tuple spaces

Abstract

While the problem of high performance packet classification has received a great deal of attention in recent years, the research community has yet to develop algorithmic methods that can overcome the drawbacks of TCAM-based solutions. This paper introduces a hybrid approach, which partitions the filter set into subsets that are easy to search efficiently. The partitioning strategy groups filters that are close to one another in tuple space [10], which makes it possible to use information from single field lookups to limit the number of subsets that must be searched. We can tradeoff running time against space consumption by adjusting the coarseness of the tuple space partition. We find that for two-dimensional filter sets, the method finds the best- matching filter with just four hash probes while limiting the memory space expansion factor to about 2. We also introduce a novel method for Longest Prefix Matching (LPM), which we use as a component of the overall packet classification algorithm. Our LPM method uses a small amount of on-chip memory to speedup the search of an off-chip data structure, but uses significantly less on-chip memory than earlier methods based on Bloom filters.