Download 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
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Packet classification is an enabling
technology
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A classifier example (5 tuples)
[Figure from Professor Jonathan Chao’s book]
Background

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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 !
ClassBench
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A suite of tools for benchmarking
packet classification algorithms and
devices.
Consists of three tools:
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Filter Set Analyzer
Filter Set Generator
Trace Generator
ClassBench Architecture
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General approach
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Construct a set of
benchmark parameter
files
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Generate a synthetic
filter set
Generate a sequence
of packet headers
Analysis of Seed Filter Sets
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Understanding Filter Composition
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We can view a filter as having two major
components:
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An address prefix pair
An application specification
Address prefix pair
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Identifies the communicating subnets by
specifying a source address prefix and a
destination address prefix.
Analysis of Seed Filter Sets
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Application Specification
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Identifies a specific application session by
specifying the transport protocol, source port
number, and destination port number.
Address prefix pair
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The speed and efficiency of several longest
prefix matching and packet classification
algorithms depend upon
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The number of unique prefix lengths
The distribution of filters across those unique
values.
Analysis of Seed Filter Sets
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The number of unique prefix lengths
acl: access control list
fw: firewall
ipc: IP chain
Analysis of Seed Filter Sets
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The distribution of filters across those unique
values
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Real filter sets have unique prefix pair distributions that
reflect the types of filters contained in the filter set.
acl5:
Analysis of Seed Filter Sets
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2 Distributions to facilitate construction of
synthetic filter sets that accurately model
seed filter sets:
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Branching Probability Distribution
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For each level in the tree, we compute the
probability that a node has one child or two children.
Skew Distribution

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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
Analysis of Seed Filter Sets
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Skew Distribution
Weight( Light )
 L
Skew  1 
Weight Heavy
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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.
Analysis of Seed Filter Sets
Branching Probability
Distribution
Skew Distribution
Analysis of Seed Filter Sets
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Application Specification
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3 useful characteristics
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Protocol:
Analysis of Seed Filter Sets
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Port Ranges
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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
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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.
Parameter Files
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Given a seed filter set, the Filter Set
Analyzer generates a parameter file.
Parameter files contain
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Statistics and probability distributions that
allow the Filter Set Generator to produce a
synthetic filter set.
Parameter Files
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Parameter files includes
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Protocol specifications and the distribution
of filters over those values.
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Port Pair Class Matrix
Prefix pair length
Synthetic Filter Set Generator
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High-level input parameters
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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.
Trace Generator
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Benchmarking a particular packet
classification algorithm or device
We must exercise the algorithm or
device using a sequence of synthetic
packet headers.
Comment
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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?