PC-TRIO: A Power Efficient TCAM Architecture for Packet Classifiers

Banerjee, Tania; Sahni, Sartaj; Seetharaman, Gunasekaran

doi:10.1109/tc.2014.2315645

Cited by 14 publications

(3 citation statements)

References 35 publications

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“…This improvement aims to reduce the number of TCAM accesses by comparing more rules in each SRAM access. For commodity SRAM chips, in which the width of an SRAM word is 512 bits [55], each SRAM word can store up to three IPv4 rules. For the rules stored in the same SRAM entry, if one of their corresponding TCAM entries matches the search key, all the rules are retrieved from SRAM for further comparisons.…”

Section: Improvement For Overlapping Rulesmentioning

confidence: 99%

Packet Classification Using TCAM of Narrow Entries

Lin,

Pan,

Wang

2023

Technologies

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Packet classification based on rules of packet header fields is the key technology for enabling software-defined networking (SDN). Ternary content addressable memory (TCAM) is a widely used hardware for packet classification; however, commercially available TCAM chips have only limited storage. As the number of supported header fields in SDN increases, the number of supported rules in a TCAM chip is reduced. In this work, we present a novel scheme to enable packet classification using TCAM with entries that are narrower than rules by storing the most representative field of a ruleset in TCAM. Due to the fact that not all rules can be distinguished using one field, our scheme employs a TCAM-based multimatch packet classification technique to ensure correctness. We further develop approaches to reduce redundant TCAM accesses for multimatch packet classification. Although our scheme requires additional TCAM accesses, it supports packet classification upon long rules with narrow TCAM entries, and drastically reduces the required TCAM storage. Our experimental results show that our scheme requires a moderate number of additional TCAM accesses and consumes much less storage compared to the basic TCAM-based packet classification. Thus, it can provide the required scalability for long rules required by potential applications of SDN.

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Section: Improvement For Overlapping Rulesmentioning

confidence: 99%

Packet Classification Using TCAM of Narrow Entries

Lin,

Pan,

Wang

2023

Technologies

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“…The representative algorithms are Set-Pruning Trie[10], Extended Grid of Trie with Path Compression[11], Rectangle Search[12], Parallel Distributed Combination Bloom Filter [13], Area-based Quad-Trie[14] and so on.…”

Section: Related Workmentioning

confidence: 99%

“…Existing basic data structure packet classification algorithms are mainly divided into trie-based packet classification algorithms, tuple space-based packet classification algorithms and Bloom Filter- based packet classification algorithms. The representative algorithms are Set-Pruning Trie[ 10 ], Extended Grid of Trie with Path Compression[ 11 ], Rectangle Search[ 12 ], Parallel Distributed Combination Bloom Filter [ 13 ], Area-based Quad-Trie[ 14 ] and so on.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical trie packet classification algorithm based on expectation-maximization clustering

Zhao

2017

PLoS ONE

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With the development of computer network bandwidth, packet classification algorithms which are able to deal with large-scale rule sets are in urgent need. Among the existing algorithms, researches on packet classification algorithms based on hierarchical trie have become an important packet classification research branch because of their widely practical use. Although hierarchical trie is beneficial to save large storage space, it has several shortcomings such as the existence of backtracking and empty nodes. This paper proposes a new packet classification algorithm, Hierarchical Trie Algorithm Based on Expectation-Maximization Clustering (HTEMC). Firstly, this paper uses the formalization method to deal with the packet classification problem by means of mapping the rules and data packets into a two-dimensional space. Secondly, this paper uses expectation-maximization algorithm to cluster the rules based on their aggregate characteristics, and thereby diversified clusters are formed. Thirdly, this paper proposes a hierarchical trie based on the results of expectation-maximization clustering. Finally, this paper respectively conducts simulation experiments and real-environment experiments to compare the performances of our algorithm with other typical algorithms, and analyzes the results of the experiments. The hierarchical trie structure in our algorithm not only adopts trie path compression to eliminate backtracking, but also solves the problem of low efficiency of trie updates, which greatly improves the performance of the algorithm.

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Hardware-based multi-match packet classification in NIDS: an overview and novel extensions for improving the energy efficiency of TCAM-based classifiers

Srinivasavarma

Pydi

2022

J Supercomput

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PC-TRIO: A Power Efficient TCAM Architecture for Packet Classifiers

Cited by 14 publications

References 35 publications

Packet Classification Using TCAM of Narrow Entries

Packet Classification Using TCAM of Narrow Entries

Hierarchical trie packet classification algorithm based on expectation-maximization clustering

Hardware-based multi-match packet classification in NIDS: an overview and novel extensions for improving the energy efficiency of TCAM-based classifiers

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