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A Memory-Efficient and Modular Approach for Large-Scale String

Pattern Matching

Author:Hoang Le, Viktor K. PrasannaPublisher: IEEE Transactions on Computers, 2012Presenter:Zi-Yang OuDate:2012/02/29

Introduction

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An algorithm called leaf-attaching to efficiently disjoint a given dictionary without increasing the number of patterns.

An architecture that achieves a memory efficiency of 0.56 (for Rogets) and 1.32 byte/char (for Snort). State-of-the-art designs can only

achieve the memory efficiency of over 2 byte/char in the best case.

The implementation on ASIC and FPGA shows a sustained aggregated throughput of 24 Gbps and 3.2 Gbps, respectively.

The design can be duplicated to improve the throughput by exploiting its simple architecture.

Definitions

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Leaf-Attaching Algorithm

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Leaf-Attaching Algorithm

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BST String Matching Algorithm

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Memory Efficiency of The BST String Matching Algorithm

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Cascading approach

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Cascading approach

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Cascading approach

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Arbitrary-Length String Matching Algorithm

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Arbitrary-Length String Matching Algorithm

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Overall Architecture

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Pattern Matching Module (PMM) Architecture

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Label Matching Module (LMM) Architecture

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Dictionary Update (1) pattern deletion

-(a) including more than one pattern

-(b) including only one pattern

-lazy deletion

-complete deletion

(2) new pattern insertion

-has parent pattern(s)

-has no parent pattern

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Modular Extensibility horizontally

vertically

-intra-stream

-inter-stream

both

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Experimental Setup

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Memory Efficiency

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The window size L should be greater than or equal to the matching latency of the LMM.

Hence, 3 values of L(16, 20, 24) are used in our analysis.

Memory Efficiency

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Throughput

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Performance Comparison

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