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A Decomposition-Based Approach for Scalable Many-Field Packet Classification on Multi-core Processors

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Abstract

As a kernel function in network routers, packet classification requires the incoming packet headers to be checked against a set of predefined rules. There are two trends for packet classification: (1) to examine a large number of packet header fields, and (2) to use software-based solutions on multi-core general purpose processors and virtual machines. Although packet classification has been widely studied, most existing solutions on multi-core systems target the classic 5-field packet classification; it is not easy to scale up their performance with respect to the number of packet header fields. In this work, we present a decomposition-based packet classification approach; it supports large rule sets consisting of a large number of packet header fields. In our approach, range-tree and hashing are used to search the fields of the input packet header in parallel. The partial results from all the fields are represented in rule ID sets; they are merged efficiently to produce the final match result. We implement our approach and evaluate its performance with respect to overall throughput and processing latency for rule set size varying from 1 to 32 K. Experimental results on state-of-the-art 16-core platforms show that, an overall throughput of 48 million packets per second and a processing latency of 2,000 ns per packet can be achieved for a 32 K rule set.

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Notes

  1. Strictly speaking, OpenFlow table lookup is designed at the packet flow level; if we assume each packet flow consists of many packets, the actual throughput in MPPS or Gbps achieved in this work is quite high. To make fair claims, we view each packet flow as a large packet in a broad sense.

  2. Note the search phase in our approach is identical to the search phase in the BV approach. The impact of \(q^{(k)}\) on the performance is similar with respect to various merging techniques.

  3. Saturating SIP and DIP fields is too pessimistic for many-field packet classification, since a saturated SIP or DIP field has \(N\) unique IP prefixes.

  4. This is not a fair metric, since processing multiple packets in parallel does not necessarily reduce the processing latency for each single packet.

  5. In decision-tree-based approaches, for a \(K\)-field rule set, each cut in one field can result in \(2^K\) new rules in the worst case.

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Authors and Affiliations

  1. University of Southern California, 3740 McClintock Ave., EEB 244, Los Angeles, CA, 90089, USA

    Yun R. Qu & Shijie Zhou

  2. University of Southern California, 3740 McClintock Ave., EEB 200C, Los Angeles, CA, 90089, USA

    Viktor K. Prasanna

Authors
  1. Yun R. Qu
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  2. Shijie Zhou
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  3. Viktor K. Prasanna
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Correspondence to Yun R. Qu.

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Supported by U.S. National Science Foundation under Grant ACI-1339756.

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Qu, Y.R., Zhou, S. & Prasanna, V.K. A Decomposition-Based Approach for Scalable Many-Field Packet Classification on Multi-core Processors. Int J Parallel Prog 43, 965–987 (2015). https://doi.org/10.1007/s10766-014-0325-6

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