Authors : Baohua Yang, Jeffrey Fong, Weirong Jiang, Yibo Xue, and Jun Li.
Publisher : IEEE TRANSACTIONS ON COMPUTERS - 2012
Presenter : Chai-Yi Chu
Date : 2012/11/14
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Introduction
Related Work
Algorithm
Evaluation
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Dynamic Discrete Bit Selection
◦ 𝐷 2 𝐵𝑆
◦ Packet classification algorithm
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64-byte Ethernet packet and 10K ACL ruleset
◦ 10 Gbps on Cavium OCTEON CN5860 multi-core network
processor
◦ 135 Gbps on Xilinx Virtex-5 FPGA
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Two major types of packet classification algorithm
◦ Searching space partition
 partition the searching space into smaller subspaces
 Ex. RFC, HSM
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◦ Ruleset partition
 cut the large ruleset into smaller ones
 Ex. HiCuts, HyperCuts
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Utilize dynamic heuristics to split the ruleset efficiently
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Combine different data structures to optimize both time
and space performance
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Terminology
◦ E-Bits
 some bits will partition the ruleset more “effectively”.
 To partition ruleset into smaller sub-rulesets.
◦ M-Vector
 Definition 3.1 (M-Vector): A M-Vector V is a bit vector that
satisfies: V[i] = 1 only if bit i is an E-Bit, otherwise V[i] = 0.
0
1
0
1
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D-Table
◦ D-Table
S-Block
 Dynamic Indexing Table T.
 For n bits, T consists of 2𝑛 cells, where each cell stores a pointer
respectively.
◦ S-Block
 memory blocks that are pointed by T’s cell.
 Each S-Block stores a subset of the ruleset R.
 Rules are stored orderly from high to low by priority.
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Preparation Phase
◦ 1. E-Bits selection
◦ 2. M-Vector generation
◦ 3. D-Table construction
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1. E-Bits selection
◦ Two problems
 E-Bits choosing
 Length optimization
◦ Solutions
 Judging Function(J-Function)
 Performance Function(P-Function)
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J-Function
◦ To judge which bits are E-Bits
 minimizing the maximum size of all subsets
 𝑒 = E-Bits set
 𝑁𝑢𝑚𝑅𝑢𝑙𝑒 𝑅𝑖 = 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑟𝑢𝑙𝑒𝑠 𝑖𝑛 𝑅𝑖
 minimizing the size of sub searching space
 𝑁𝑢𝑚𝑆𝑢𝑏𝑠𝑝𝑎𝑐𝑒 𝑅𝑖 = 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑠𝑢𝑏𝑠𝑝𝑎𝑐𝑒𝑠 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑒𝑑 𝑏𝑦 𝑅𝑖
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P-Function
◦ To decide the number of E-Bits.
 𝑖. 𝑒. , to optimize the length of 𝑛.
 𝑀𝑒𝑚 𝑉 = 𝑚𝑒𝑚𝑜𝑟𝑦 𝑢𝑠𝑒𝑑 𝑏𝑦 M-Vector
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2. M-Vector generation
◦ Built with the process of E-Bits choosing and length
optimization
 Fast-Growth
 Intelli-Swap(I-Swap)
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3. D-Table construction
◦ Set up T with length 2𝑛 , each cell
T[i] stores a pointer to an empty
S-Block.
◦ Use V to mask r at the E-Bits
positions, which results in a
bit-string i of length n.
◦ Insert r into the bottom of the
S-Block pointed by T[i].
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Classification Phase
H
V
T
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Complexity Analysis
◦ ruleset R consists of N rules, and n E-Bits are selected.
2
3
◦ Time complexity is 𝑂(( )𝑛 ∗ 𝑁)
◦ Storage complexity is
4 𝑛
𝑂(( )
3
∗ 𝑁)
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Suppose the length of the D-Table is 2𝑛 .
The length of the largest S-Blocks is L.
◦ Time complexity is 𝑂(𝐿),
◦ Storage complexity is 𝑂(2𝑛 ∗ 𝐿).
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The size ratio between the subsets and the original
ruleset is ρ.
Then the size of the partitioned subsets will be ρ · N.
Thus with n E-Bits,
◦ 𝑂(𝐿) = 𝑂(ρ𝑛 · 𝑁).
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IA based Implementation
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◦ Memory storage usage
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◦ Scalability
 The ratio of Memory Per Rule (MPR)
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Multi-core NP Implementation
◦ Cavium OCTEON CN5860, ACL10K
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FPGA based Implementation
◦ Xilinx Virte-5 FPGA(XC5VSX240T), 2048 Kb of Block RAM
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