1. Packet Classification using Hierarchical Intelligent Cuttings
Pankaj Gupta and Nick McKeown
Stanford University
{pankaj,
Hot Interconnects VII
August 18, 1999

2. Outline Introduction and Motivation Overview of the proposed algorithm
Details of the algorithm
Implementation Results
Conclusions
Packet Classification using Hierarchical Intelligent Cuttings

3. Packet Classification
HEADER
----
Predicate
Action
Classifier (Policy Database)
Packet Classification
Forwarding Engine
Action
Incoming Packet

4. 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 ( , , …, TCP) would have action A2 applied to it.

5. Performance Metrics of a Classification Algorithm
Data structure storage requirements
Packet classification time
Preprocessing time
Incremental Update time

6. Previous Work

7. 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

8. Observations No single good solution for all cases.
But real classifiers have structure.
Perhaps an algorithm can exploit this structure.
A heuristic hybrid scheme ….

9. Proposed Algorithm: Basic Idea
{R1, R2, R3, …, Rn}
Decision Tree
{R1, R3,R4}
{R1, R2,R5}
{R8, Rn}
Binth: BinThreshold = Maximum Subset Size = 3

10. Example 2-D Classifier

11. Geometric View
255 R1 R7 R3
(0-31,0-255) P R2
128 R4 R6 R5
128
255

12. 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 HiCut C(v) = (dimension d, #partitions of B(v) across d)

13. HiCuts
255 R1 R7 R3 Y R2
128 R4 R6 R5 X
128
255

14. HiCuts
255 R3 Y R2
128 R4 R5 X 64
128

15. HiCut Decision Tree for binth = 2
(256 * 256, X, 4)
Packet P(65, 130)
(64*256, Y, 2) R1 R2 R2 R2 R6 R7 R4 R2 R5 R6

16. 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.

17. 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

18. 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)
rules

19. Number of Memory Accesses Number of Rules (log scale)
Crossproducting
Number of Rules (log scale)
Binth = 8, spfac = 4

20. Size of the data structure
Space in KiloBytes (log scale)
Number of Rules (log scale)
Binth = 8 ; spfac = 4

21. Comparison with Crossproducting
Space in MegaBytes (log scale)
Number of Rules (log scale)
Binth = 8 ; spfac = 4

22. Time in seconds (log scale) Number of Rules (log scale)
Preprocessing Time
Time in seconds (log scale)
Number of Rules (log scale)
Binth = 8, spfac = 4, 333MHz P-II running Linux

23. Incremental Update Time
Time in seconds (log scale)
Number of Rules (log scale)
Binth = 8, spfac = 4 , 333MHz P-II running Linux

24. Conclusions
Exploiting the structure of classifiers is important for a good solution.
The proposed HiCut packet classification scheme seems to be of practical use.

25. 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.
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