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Packet Classification using Hierarchical Intelligent Cuttings Pankaj Gupta and Nick McKeown Stanford University {pankaj, Hot Interconnects.

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Presentation on theme: "Packet Classification using Hierarchical Intelligent Cuttings Pankaj Gupta and Nick McKeown Stanford University {pankaj, Hot Interconnects."— Presentation transcript:

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

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

3 Packet Classification Action ---- PredicateAction Classifier (Policy Database) Packet Classification Forwarding Engine Incoming Packet HEADERHEADER

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 l Data structure storage requirements l Packet classification time l Preprocessing time l 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(N k ) space, or O(log k-1 N) time with O(N) space

8 Observations l No single good solution for all cases. –But real classifiers have structure. l 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 R4 R5 R3 R2 R6 R7 R1 (0-31,0-255) P

12 Decision Tree using Hierarchical Intelligent Cuttings (HiCuts) With each internal node v, associate: l A rectangle, or a box B(v) l A set of rules, CollidingRuleSet, R(v) l A HiCut C(v) = (dimension d, #partitions of B(v) across d)

13 HiCuts R4 R5 R3 R2 R6 R7 R1 Y X

14 HiCuts R4 R5 R3 R2 Y X

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

16 Heuristics to exploit classifier structure l Picking a suitable dimension to hicut across. u Minimize the maximum number of rules into any one partition, OR u Maximize the entropy of the distribution of rules across the partition, OR u Maximise the different number of specifications in one dimension l Picking the suitable number of partitions (HiCuts) to be made. u Affects the space consumed and the classification time. Tuned by a parameter, spfac.

17 Tunable Parameters l Binth, the maximum size of the set of rules at each leaf l Spfac, a parameter which guides the partitioning process to choose the number of partitions

18 Implementation Results: Four dimensional real-life classifiers l 40 access-lists taken from real ISP and enterprise networks l Four dimensions: (Src IP, Dst IP, L4 protocol, L4 destination port) l rules

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

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

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

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

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

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

25 In the paper... l Explanation of the heuristics used in building the HiCut decision tree. l Detailed implementation results. l Effect of the parameters binth and spfac on the depth and space characteristics. l Available at: l


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