1. 1 TCAM Razor: A Systematic Approach Towards Minimizing Packet Classifiers in TCAMs Department of Computer Science and Information Engineering National Cheng Kung University, Taiwan R.O.C. Authors: Chad R. Meiners, Alex X. Liu, Eric Torng Publisher: ICNP 2007 Present: Yu-Tso Chen Date: November, 6, 2007

2. 2 Outline 1. Introduction 2. Definitions and problem description 3. Multi-dimensional TCAM Mnimization 4. Experimental Result 5. Conclusion

3. 3 Occam ’ s Razor “Of two equivalent theories or explanations, all other things being equal, the simpler one is to be preferred.”

4. 4 Introduction TCAMs have their own limitations with respect to packet classification a) Range expansion – TCAM only store rules that are encoded in ternary format Source & Destination port numbers are specified in Ranges 30 prefixs are needed to represent the single range [1, 65534] 30 X 30 =900 TCAM entries

5. 5 Encoding Considerations Range expansions Encoding ranges has a multiplicative effect [1,14] => 0001, 001*, 01**, 10**, 110*, 1110 [1,14] /\ [1,14] => 6 × 6 = 36 entries 1 rule can lead to over 900 TCAM entries. Each port interval can leads to 30 rules w-bit interval => 2w-2 maximum entries Prefix Encoding Standard practice to encode entries in prefix format Each field is in prefix format Minimizing arbitrary ternary lists is NP-hard

6. 6 TCAM Challenges b) Low capacity – Maximum of 18 Mb – 2 Mb and 1Mb modules are the most popular – Each entry has 144 bits c) Larger capacity consumes more energy – More power, more heat – Cooling is a concern d) Larger capacity consumes more board space e) Larger capacity is more expensive – 1Mb module is ~ $250

7. 7 Outline 1. Introduction 2. Definitions and problem description 3. Matching of Individual Patterns 4. Selective Grouping of Multiple Patterns 5. Evaluation Result 6. Conclusion

8. 8 Definitions and problem description Reducing the number of rules in a TCAM directly 900 entries 6 entries

9. 9 Our solution ： TCAM Razor Above problem is NP-hard, we using three techniques Decision diagrams Dynamic programming Redundancy removal

10. 10 Our solution ： TCAM Razor Our solution consists of four basic step 1)Convert a given packet classifier to a reduced decision diagram 2)Every non-terminal node in the decision diagram minimize the number of prefixs associated with its outgoing edges using dynamic programming 3)Generate rules from the decision diagram 4)Remove redundant rules

11. 11 Outline 1. Introduction 2. Definitions and problem description 3. Multi-dimensional TCAM Mnimization 4. Experimental Result 5. Conclusion

12. 12 Example one-dimensional TCAM minimization problem

13. 13 From single to Multiple dimension First step of TCAM Razor is to convert rules into an FDD (Firewall Decision Diagram)

14. 14 Multi-dimensional TCAM Minimization RuleFieldDecision 110**Accept 2****Discard RuleFieldDecision 1****Discard RuleFieldDecision 11001V3V3 210**V2V2 3****V3V3 Work from the bottom up V2V2 V3V3 V1V1

15. 15 MINIMUM PACKET CLASSIFIER 10** (with decision accept and cost 1) 0 *** (with decision discard and cost 1) 11** (with decision discard and cost 1)

16. 16 MINIMUM PACKET CLASSIFIER 1000 (with decision V 2 and cost 1) 101* (with decision V 2 and cost 1) 0 *** (with decision V 3 and cost 1) 1001 (with decision V 3 and cost 1) 11** (with decision V 3 and cost 1) RuleFieldDecision 11001V3V3 210**V2V2 3****V3V3 V1V1

17. 17 Solution Composition Redundant RuleFieldDecision 110**Accept 2****Discard RuleFieldDecision 1****Discard RuleFieldDecision 11001V3V3 210**V2V2 3****V3V3 V2V2 V3V3 V1V1 RuleF1F1 F2F2 Decision 11001****Discard 210** Accept 310******Discard 4**** Discard

18. 18 Outline 1. Introduction 2. Definitions and problem description 3. Multi-dimensional TCAM Mnimization 4. Experimental Result 5. Conclusion

19. 19 Experimental Data Real-life Packet Classifiers 42 actual classifiers 17 structurally distinct classifiers Synthetic Packet Classifiers Difficult to get real-life packet classifiers 18 sets of 100 uniformly sized classifiers Randomly generated Generated to look like real classifiers

20. 20 Experimental Metrics Direct Rule Expansion Direct(f) Number of rules produced by encoding classifier f into prefix format Each rule is the expansion of the minimal prefix representations of each interval Algorithm application A(f) Number of prefix rules produced applying algorithm A on classifier f TCAM Razor Redundancy Removal

21. 21 Experimental Metrics For a set of classifiers S

22. 22 Experimental Factors Field Ordering FDD field order results in a substantial difference 5! = 120 permutations Permutation 49 is the best with an average compression of 18.2% (Source IP, Protocol type, Dest. IP, Dest. Port, Source Port) TCAM Razor(B) – TCAM Razor using the best of the permutations for f

23. 23 Compression Ratios TCAM Razor: 18.2% average compression ratio Redundancy Removal: 41.8%

24. 24 Compression Ratios 13 of 17 classifiers have less than 1%

25. 25 Expansion Ratio

26. 26 Synthetic Packet Classifiers Average compression ratio –.046 Total compression ratio –.016

27. 27 Synthetic Packet Classifiers Average expansion ratio – 8.737 Total expansion ratio – 3.082

28. 28 Outline 1. Introduction 2. Definitions and problem description 3. Multi-dimensional TCAM Mnimization 4. Experimental Result 5. Conclusion

29. 29 Concluding Remarks TCAM Razor usually produces significant space savings 82% reduction in TCAM entries on average No hardware modification Can be used to improve existing hardware