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Packet Classification using Hierarchical Intelligent Cuttings. Pankaj Gupta and Nick McKeown Stanford University {pankaj, nickm}@stanford.edu. Hot Interconnects VII August 18, 1999. Outline . Introduction and Motivation Overview of the proposed algorithm Details of the algorithm

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packet classification using hierarchical intelligent cuttings

Packet Classificationusing Hierarchical Intelligent Cuttings

Pankaj Gupta and Nick McKeown

Stanford University

{pankaj, nickm}@stanford.edu

Hot Interconnects VII

August 18, 1999

outline
Outline
  • Introduction and Motivation
  • Overview of the proposed algorithm
  • Details of the algorithm
  • Implementation Results
  • Conclusions

Packet Classification using Hierarchical Intelligent Cuttings

packet classification

Forwarding Engine

Packet Classification

Classifier (Policy Database)

Predicate

Action

----

----

----

----

----

----

Packet Classification

HEADER

Action

Incoming Packet

multi field packet classification
Multi-field Packet Classification

Given a classifier with N rules, find the action associated with the highest priority rule matching an incoming packet.

Example: A packet (152.168.3.32, 152.163.171.71, …, TCP) would have action A2 applied to it.

performance metrics of a classification algorithm
Performance Metrics of a Classification Algorithm
  • Data structure storage requirements
  • Packet classification time
  • Preprocessing time
  • Incremental Update time
bounds from computational geometry
Bounds from Computational Geometry

Point Location among N non-overlapping regions in k dimensions takes

either

O(log N) time with O(Nk) space,

or

O(logk-1N) time with O(N) space

observations
Observations
  • No single good solution for all cases.
    • But real classifiers have structure.
  • Perhaps an algorithm can exploit this structure.
    • A heuristic hybrid scheme ….
proposed algorithm basic idea
Proposed Algorithm: Basic Idea

{R1, R2, R3, …, Rn}

Decision Tree

{R1, R3,R4}

{R1, R2,R5}

{R8, Rn}

Binth: BinThreshold = Maximum Subset Size = 3

geometric view

P

(0-31,0-255)

Geometric View

255

R1

R7

R3

R2

128

R4

R6

R5

0

0

128

255

decision tree using hierarchical intelligent cuttings hicuts
Decision Tree using Hierarchical Intelligent Cuttings (HiCuts)

With each internal node v, associate:

  • A rectangle, or a box B(v)
  • A set of rules, CollidingRuleSet, R(v)
  • A HiCutC(v) = (dimension d, #partitions of B(v) across d)
hicuts
HiCuts

255

R1

R7

R3

Y

R2

128

R4

R6

R5

0

X

0

128

255

hicuts14
HiCuts

255

R3

Y

R2

128

R4

R5

0

X

64

128

slide15

Packet P(65, 130)

HiCut Decision Tree for binth = 2

(256 * 256, X, 4)

(64*256, Y, 2)

R1

R2

R2

R2

R6

R7

R4

R2

R5

R6

heuristics to exploit classifier structure
Heuristics to exploit classifier structure
  • Picking a suitable dimension to hicut across.
      • Minimize the maximum number of rules into any one partition, OR
      • Maximize the entropy of the distribution of rules across the partition, OR
      • Maximise the different number of specifications in one dimension
  • Picking the suitable number of partitions (HiCuts) to be made.
      • Affects the space consumed and the classification time. Tuned by a parameter, spfac.
tunable parameters
Tunable Parameters
  • Binth, the maximum size of the set of rules at each leaf
  • Spfac, a parameter which guides the partitioning process to choose the number of partitions
implementation results four dimensional real life classifiers
Implementation Results: Four dimensional real-life classifiers
  • 40 access-lists taken from real ISP and enterprise networks
  • Four dimensions: (Src IP, Dst IP, L4 protocol, L4 destination port)
  • 100-1733 rules
slide19

Crossproducting

Number of Memory Accesses

Number of Rules (log scale)

Binth = 8, spfac = 4

size of the data structure
Size of the data structure

Space in KiloBytes (log scale)

Number of Rules (log scale)

Binth = 8 ; spfac = 4

comparison with crossproducting
Comparison with Crossproducting

Space in MegaBytes (log scale)

Number of Rules (log scale)

Binth = 8 ; spfac = 4

preprocessing time
Preprocessing Time

Time in seconds (log scale)

Number of Rules (log scale)

Binth = 8, spfac = 4, 333MHz P-II running Linux

incremental update time
Incremental Update Time

Time in seconds (log scale)

Number of Rules (log scale)

Binth = 8, spfac = 4 , 333MHz P-II running Linux

conclusions
Conclusions
  • Exploiting the structure of classifiers is important for a good solution.
  • The proposed HiCut packet classification scheme seems to be of practical use.
in the paper
In the paper...
  • Explanation of the heuristics used in building the HiCut decision tree.
  • Detailed implementation results.
  • Effect of the parameters binth and spfac on the depth and space characteristics.
  • Available at:

http://www-cs-students.stanford.edu/~pankaj/research.html

  • Email: pankaj@stanford.edu