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SSA: a power and memory efficient scheme to multi-match packet classification

Published: 26 October 2005 Publication History
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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

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      cover image ACM Conferences
      ANCS '05: Proceedings of the 2005 ACM symposium on Architecture for networking and communications systems
      October 2005
      230 pages
      ISBN:1595930825
      DOI:10.1145/1095890
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      Published: 26 October 2005

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      Author Tags

      1. TCAM
      2. energy-efficient design
      3. multiple-match
      4. packet classification

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      • (2024)In-Network ACL Rules Placement using Deep Reinforcement Learning2024 IEEE International Mediterranean Conference on Communications and Networking (MeditCom)10.1109/MeditCom61057.2024.10621188(341-346)Online publication date: 8-Jul-2024
      • (2023)Packet Classification Using TCAM of Narrow EntriesTechnologies10.3390/technologies1105014711:5(147)Online publication date: 19-Oct-2023
      • (2023)Automated Placement of In-Network ACL Rules2023 IEEE 9th International Conference on Network Softwarization (NetSoft)10.1109/NetSoft57336.2023.10175436(486-491)Online publication date: 19-Jun-2023
      • (2023)ISAC: In-Switch Approximate Cache for IoT Object Detection and RecognitionIEEE INFOCOM 2023 - IEEE Conference on Computer Communications10.1109/INFOCOM53939.2023.10229067(1-10)Online publication date: 17-May-2023
      • (2023)TCAM-based packet classification for many-field rules of SDNsComputer Communications10.1016/j.comcom.2023.03.001203(89-98)Online publication date: Apr-2023
      • (2019)Optimized Programming for STT-MTJ-Based TCAM for Low-Energy Approximate ComputingApplications of Emerging Memory Technology10.1007/978-981-13-8379-3_6(159-176)Online publication date: 17-Jul-2019
      • (2018)Fast TCAM-Based Multi-Match Packet Classification Using DiscriminatorsIEEE Transactions on Multi-Scale Computing Systems10.1109/TMSCS.2018.28476774:4(686-697)Online publication date: 1-Oct-2018
      • (2018)CMAIEEE Computer Architecture Letters10.1109/LCA.2017.271902317:1(33-36)Online publication date: 1-Jan-2018
      • (2016)TCAM-Based Packet Classification Using Multi-stage SchemeProceedings of the Fifth International Conference on Network, Communication and Computing10.1145/3033288.3033302(83-87)Online publication date: 17-Dec-2016
      • (2016)Tcam-based multi-match packet classification using multidimensional rule layeringIEEE/ACM Transactions on Networking10.1109/TNET.2015.241127424:2(1125-1138)Online publication date: 1-Apr-2016
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