Fast Packet Classification Using Bit Compression with Fast

Fast Packet Classification Using Bit Compression with Fast Boolean Expansion Author: Chien Chen, Chia-Jen Hsu and Chi-Chia Huang Publisher: Journal of Information Science and Engineering, 2007 Presenter: Chun-Yi Li Date: 2009/03/11

Outline o Related Work n Bitmap intersection n Aggregated Bit Vector (ABV) o Bit Compression Algorithm o Fast Boolean Expasion o Performance 2

Related Work Bitmap intersection u. Each interval associated with an N-bits bit vector. R 5 R 6 R 3 R 4 R 2 R 1 1 2 3 4 5 6 0 0 0 1 0 1 1 1 0 0 1 1 0 0 0 0 1 1 0 0 0 3

Related Work Aggregated Bit Vector (ABV) Rule Field 1 0 Field 2 1 Field 1 R 0 00* 0000 1110 0000 0001 100 R 1 00* 010 011 R 2 10* 11* R 3 11* 10* R 4 0* 10* R 5 0* 11* R 6 0* 0* R 7 1* 0000 0010 110 R 8 1* 0* 011 R 9 11* 0* R 10 10* 0 0 1100 1110 0010 0001 101 0001 110 111 110 0 1 1 0 Field 2 1 Aggregate size = 4 1000 0010 110 0100 0011 110 0001 1000 0010 0100 000 111 111 110

Related Work Aggregated Bit Vector (ABV) o Aggregation tries to decrease the memory access time by adding ABV. o Generates false matching. - Rule rearrangement. o Faster than bitmap intersection, but use more space. 5

Outline o Related Work n Bitmap intersection n Aggregated Bit Vector (ABV) o Bit Compression Algorithm o Fast Boolean Expasion o Performance 7

Bit Compression Algorithm o Memory storage - θ(d. N㏒N) o Require additional time for decompression 8

Bit Compression Algorithm Construct Don’t Care Vectors (DCV) Removing the redundant “ 1” bits Don’t Care Vectors (DCV) 0 0 0 0 0 1 1 9

Bit Compression Algorithm Removing redundant ‘ 0’ bits 10

Bit Compression Algorithm Construct Compressed Bit Vector(CBV) Append “index table lookup address” (ITLA) For convience of memory access, fill up ‘ 0’ to the end of the CBVs and index table. 11

Bit Compression Algorithm Construct index table 00 01 10 11 Index table 1 3 4 0 1 2 5 6 1 2 7 0 9 10 0 8 0 0 Filled up with ‘ 0’ 12

Bit Compression Algorithm Search (DCV) 13

Bit Compression Algorithm Maxmum Overlap Analysis β – denote the probability that PA is a prefix of PB. (PA and PB are randomly selected from the rule table) 14

Region Segmentation The region segmentation algorithm constructs an undirected graph first. Each vertex vi corresponds to a rule Ri, and an edge is constructed between vi and vj if rules i and j are dependent. 15

Region Segmentation 1. Find connected component. 2. Remove maximum degree vectex if set smaller than maximum overlap. Maximum overlap = 5 STEP 1: C 1 {1, 2, 3, 4, 5, 6, 7, 8} C 2 {9, 10} STEP 2: C 11 {1, 3, 4} C 12 {1, 2, 5, 6, 7, 8} C 2 {9, 10} STEP 3: C 11 {1, 3, 4} C 121 {1, 2, 5, 6, 8} C 122 {1, 2, 6, 7} C 2 {9, 10} 16

Merge Rule Set Two rule sets can be merged together if the rule numbers of the merged rule sets are smaller than or equal to the maximum overlap. CR 1 {1, 3, 4} CR 2 {1, 2, 5, 6, 8} CR 3 {1, 2, 6, 7} CR 4 {9, 10} 00 01 10 11 Index table 1 3 4 0 1 2 5 6 1 2 6 7 9 10 0 0 Merge 0 8 0 0 CR 1 {1, 3, 4. 9, 10} CR 2 {1, 2, 5, 6, 8} CR 3 {1, 2, 6, 7} New index table 00 1 3 4 9 10 01 1 2 5 6 8 10 1 2 6 7 0 17

Merge Rule Set New index table 00 1 3 4 9 10 01 1 2 5 6 8 10 1 2 6 7 0 18

Merge Rule Set 19

Outline o Related Work n Bitmap intersection n Aggregated Bit Vector (ABV) o Bit Compression Algorithm o Fast Boolean Expasion o Performance 20

Fast Boolean Expasion(FBE) o Original boolean expression: (CBVS+DCVS)*(CBVD+DCVD) o Modify boolean expression: (CBVS*CBVD)+(CBVS*DCVD)+ (DCVS*CBVD)+(DCVS*DCVD) Takes few memory accesses since CBVS and CBVD are compressed bit vector. Only extract the essential bits from DCV that are corresponding to the set bits of CBV Default rule 21

Outline o Related Work n Bitmap intersection n Aggregated Bit Vector (ABV) o Bit Compression Algorithm o Fast Boolean Expasion o Performance 22

Performance 23

Performance 24

Performance Transmission rate Without wildcard rule (K) 25

Performance Transmission rate Contain 20% wildcard rule (K) 26

Performance Transmission rate Contain 50% wildcard rule (K) 27