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SSA: a power and memory efficient scheme to multi-match packet classification
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Source Symposium On Architecture For Networking And Communications Systems archive
Proceedings of the 2005 ACM symposium on Architecture for networking and communications systems table of contents
Princeton, NJ, USA
SESSION: IP lookup and packet classification table of contents
Pages: 105 - 113  
Year of Publication: 2005
ISBN:1-59593-082-5
Authors
Fang Yu  University of California Berkeley, Berkeley, CA
T. V. Lakshman  Bell Laboratories, Lucent Technologies
Martin Austin Motoyama  University of California Berkeley, Berkeley, CA
Randy H. Katz  University of California Berkeley, Berkeley, CA
Sponsors
SIGCOMM: ACM Special Interest Group on Data Communication
ACM: Association for Computing Machinery
SIGARCH: ACM Special Interest Group on Computer Architecture
Publisher
ACM  New York, NY, USA
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ABSTRACT

New network applications like intrusion detection systems and packet-level accounting require multi-match packet classification, where all matching filters need to be reported. Ternary Content Addressable Memories (TCAMs) have been adopted to solve the multi-match classification problem due to their ability to perform fast parallel matching. However, TCAM is expensive and consumes large amounts of power. None of the previously published multi-match classification schemes is both memory and power efficient. In this paper, we develop a novel scheme that meets both requirements by using a new Set Splitting Algorithm (SSA). The main idea of SSA is that it splits filters into multiple groups and performs separate TCAM lookups into these groups. It guarantees the removal of at least half the intersections when a filter set is split into two sets, thus resulting in low TCAM memory usage. SSA also accesses filters in the TCAM only once per packet, leading to low power consumption. We compare SSA with two best known schemes: MUD [1] and Geometric Intersection-based solutions [2]. Simulation results based on the SNORT filter sets show that SSA uses approximately the same amount of TCAM memory as MUD, but yields a 75% to 95% reduction in power consumption. Compared with Geometric Intersection-based solutions, SSA uses 90% less TCAM memory and power at the cost of one additional TCAM lookup per packet.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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Collaborative Colleagues:
Fang Yu: colleagues
T. V. Lakshman: colleagues
Martin Austin Motoyama: colleagues
Randy H. Katz: colleagues