Packet classification using coarse-grained tuple spaces

  • Authors:
  • Haoyu Song;Jonathan Turner;Sarang Dharmapurikar

  • Affiliations:
  • Washington University, St. Louis, MO;Washington University, St. Louis, MO;Washington University, St. Louis, MO

  • Venue:
  • Proceedings of the 2006 ACM/IEEE symposium on Architecture for networking and communications systems
  • Year:
  • 2006

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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 trade-off 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.