1. Scalable Packet Classification Using Hybrid and Dynamic Cuttings Authors : Wenjun Li,Xianfeng Li Publisher : Engineering Lab on Intelligent Perception for Internet of Things (ELIP) Presenter : Kai-Hsun Li Date : 2014/12/24 Department of Computer Science and Information Engineering National Cheng Kung University, Taiwan R.O.C.

2. Introduction Decision-tree based schemes are the most well-known solutions for packet classification. HiCuts and HyperCuts, suffer from memory explosion problem. EffiCuts, suffers from excessive memory accesses. In this paper, propose HD-Cuts use hybrid and dynamic cuttings to improve memory accesses and performance simultaneously National Cheng Kung University CSIE Computer & Internet Architecture Lab 2

3. Observation National Cheng Kung University CSIE Computer & Internet Architecture Lab 3

4. Proposed Scheme(1/6) - Rule Set Partitioning(1/2) National Cheng Kung University CSIE Computer & Internet Architecture Lab 4 partition the rules into the following four subsets to separate large rules. 1) Subset_SA: rules with wildcard in SA. 2) Subset_DA: rules with wildcard in DA. 3) Subset_Small: rules with no wildcard in SA and DA. 4) Subset_Large: rules with wildcard in both SA and DA.

5. Proposed Scheme(2/6) - Rule Set Partitioning(2/2) National Cheng Kung University CSIE Computer & Internet Architecture Lab 5 thresh_large = 0.5 Subset_SA = {R1, R3, R5, R7, R9, R10, R13}

6. Proposed Scheme(2/6) - Rule Set Partitioning(2/2) National Cheng Kung University CSIE Computer & Internet Architecture Lab 6 thresh_large = 0.5 Subset_SA = {R1, R3, R5, R7, R9, R10, R13} Subset_DA ={R2, R4, R6, R8}

7. Proposed Scheme(2/6) - Rule Set Partitioning(2/2) National Cheng Kung University CSIE Computer & Internet Architecture Lab 7 thresh_large = 0.5 Subset_SA = {R1, R3, R5, R7, R9, R10, R13} Subset_DA ={R2, R4, R6, R8} Subset_Small = {R11, R12}

8. Proposed Scheme(2/6) - Rule Set Partitioning(2/2) National Cheng Kung University CSIE Computer & Internet Architecture Lab 8 thresh_large = 0.5 Subset_SA = {R1, R3, R5, R7, R9, R10, R13} Subset_DA ={R2, R4, R6, R8} Subset_Small = {R11, R12} Subset_Large = {R14}

9. Proposed Scheme(3/6) -Hybrid and Dynamic Cuttings (1/4) National Cheng Kung University CSIE Computer & Internet Architecture Lab 9 Subset_SA => step1. conduct HiCuts by considering SA 、 DA. step2. if next cut is not “DA “ or rules in current node rules > binth then extend to five field with HyperCut. Subset_DA => step1. conduct HiCuts by considering SA 、 DA. step2. if next cut is not “SA “ or rules in current node > binth then extend to five field with hypercut. Subset_Small => step1.conduct HyperCuts by considering SA 、 DA. step2.if rule replications become serious or rules in current node rules > binth then extend to five field with HyperCut. Subset_Large => HyperCut considering all five fields simultaneously.

10. Proposed Scheme(4/6) -Hybrid and Dynamic Cuttings (2/4) National Cheng Kung University CSIE Computer & Internet Architecture Lab 10 Subset_SA = {R1, R3, R5, R7, R9, R10, R13}

11. Proposed Scheme(5/6) -Hybrid and Dynamic Cuttings (3/4) National Cheng Kung University CSIE Computer & Internet Architecture Lab 11 Root (Hicuts) (DA_cuts:4) Leaf 1 R1 Leaf 2 R3 Node 1 R5,R7,R9,R13 Leaf 3 R13 rules in current node rules > binth => extend to five field with hypercut

12. Proposed Scheme(6/6) -Hybrid and Dynamic Cuttings (4/4) National Cheng Kung University CSIE Computer & Internet Architecture Lab 12 Root (Hicuts) (DA_cuts:4) Leaf 1 R1 Leaf 2 R3 Node 1 (Hypercuts) (SA_cuts:1) (DA_cuts:1) (SP_cuts:2) (DP_cuts:2) (Port_cuts:1) Leaf 3 R13 Leaf 4 R5 Leaf 5 R7 Leaf 6 R9 Leaf 7 R10

13. Experiment Results(1/2) National Cheng Kung University CSIE Computer & Internet Architecture Lab 13 Total Memory for HiCuts, HyperCuts, EffiCuts and HD-Cuts(binth=8, thresh_large=0.05)

14. Experiment Results(2/2) National Cheng Kung University CSIE Computer & Internet Architecture Lab 14 Memory Access for HiCuts, HyperCuts, EffiCuts and HD-Cuts(binth=8, thresh_large=0.05 )