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ClassBench: A Packet Classification Benchmark

ClassBench: A Packet Classification Benchmark. David E. Taylor, Jonathan S. Turner Washington University in Saint Louis Presented by Jian-Meng Yang. Background. Packet classification is an enabling technology A classifier example (5 tuples). [Figure from Professor Jonathan Chao ’ s book].

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ClassBench: A Packet Classification Benchmark

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  1. ClassBench: A Packet Classification Benchmark David E. Taylor, Jonathan S. Turner Washington University in Saint Louis Presented by Jian-Meng Yang

  2. Background • Packet classification is an enabling technology • A classifier example (5 tuples) [Figure from Professor Jonathan Chao’s book]

  3. Background • A packet classifier must compare header fields of every incoming packet • To accelerate search time or reduce storage requirements • No standard performance evaluation tools • ClassBench is presented !

  4. ClassBench • A suite of tools for benchmarking packet classification algorithms and devices. • Consists of three tools: • Filter Set Analyzer • Filter Set Generator • Trace Generator

  5. ClassBench Architecture • General approach • Construct a set of benchmark parameter files • Generate a synthetic filter set • Generate a sequence of packet headers

  6. Analysis of Seed Filter Sets • Understanding Filter Composition • We can view a filter as having two major components: • An address prefix pair • An application specification • Address prefix pair • Identifies the communicating subnets by specifying a source address prefix and a destination address prefix.

  7. Analysis of Seed Filter Sets • Application Specification • Identifies a specific application session by specifying the transport protocol, source port number, and destination port number. • Address prefix pair • The speed and efficiency of several longest prefix matching and packet classification algorithms depend upon • The number of unique prefix lengths • The distribution of filters across those unique values.

  8. Analysis of Seed Filter Sets • The number of unique prefix lengths acl: access control list fw: firewall ipc: IP chain

  9. Analysis of Seed Filter Sets • The distribution of filters across those unique values • Real filter sets have unique prefix pair distributions that reflect the types of filters contained in the filter set. acl5:

  10. Analysis of Seed Filter Sets • 2 Distributions to facilitate construction of synthetic filter sets that accurately model seed filter sets: • Branching Probability Distribution • For each level in the tree, we compute the probability that a node has one child or two children. • Skew Distribution • For nodes with TWO children, we compute skew, which is a relative measure of the “weights” of the left and right subtrees of the node. • Weight: The number of filters specifying prefixes in the subtree

  11. Analysis of Seed Filter Sets • Skew Distribution • L • heavy be the subtree with the largest weight • light be the subtree with equal or less weight The Black nodes denote a prefix specified by a single filter. The subtrees denoted by triangles with associated weight.

  12. Analysis of Seed Filter Sets Branching Probability Distribution Skew Distribution

  13. Analysis of Seed Filter Sets • Application Specification • 3 useful characteristics • Protocol:

  14. Analysis of Seed Filter Sets • Port Ranges • WC, wildcard • HI, ephemeral user port range [1024 : 65535] • LO, well-known system port range [0 : 1023] • AR, arbitrary range • EM, exact match • Port Pair Class • The structure of source and destination port range pairs is a key point of interest for both modeling real filter sets and designing efficient search algorithms.

  15. Parameter Files • Given a seed filter set, the Filter Set Analyzer generates a parameter file. • Parameter files contain • Statistics and probability distributions that allow the Filter Set Generator to produce a synthetic filter set.

  16. Parameter Files • Parameter files includes • Protocol specifications and the distribution of filters over those values. • Port Pair Class Matrix • Prefix pair length

  17. Synthetic Filter Set Generator • High-level input parameters • size: Target size for the synthetic filter set. • smoothing: Controls the number of new address aggregates. • Scope: The measure of the number of possible packet headers covered by the filter.

  18. Trace Generator • Benchmarking a particular packet classification algorithm or device • We must exercise the algorithm or device using a sequence of synthetic packet headers.

  19. Comment • It provides the network research scientists a good tool for evaluating their packet classifier performance. • No information about how to get or generate the Seed filter set. • How about the exceptions?

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