Technology of Network & Communication
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 1
237-240,255

Decision tree based pre-classifier for energy-efficient TCAM based packet classification

Li Wenjun1,2,3
Liu Xinwei1
Xing Kaixuan1,2
Le Wenxia1
Li Hui1,2
1. Peking University Shenzhen Graduate School, Shenzhen Guangdong 518055, China
2. Peng Cheng Laboratory, Shenzhen Guangdong 518055, China
3. School of Electronic Engineering & Computer Science, Peking University, Beijing 100871, China
DOI: 10.19734/j.issn.1001-3695.2019.09.0625

Abstract

Due to the high-speed requirement of high-end network devices, hardware using TCAMs has been the dominant implementation of packet classification in industry. Despite its capability for line-speed queries, TCAM is not only power hungry but also capacity inefficient. By making use of a pre-classifier to activate TCAM blocks selectively, many research efforts significantly reduce the power consumption of TCAM. However, these bottom-up based pre-classifiers achieve power savings at the expense of poor utilization of TCAM capacity, and the potential of power reduction is not fully exploited in many cases. This paper proposed a power-saving pre-classifier for TCAM based packet classification, which constructed based on decision trees. By grouping rules with respect to their small fields, rules could be recursively mapped into decision trees without the trouble of rule replications, so that a top-down traversal algorithm could be well applied for obtaining index items. Experimental results show that for rule sets up to one hundred thousand entries, the proposed design achieves 98.2% power reduction with a TCAM storage overhead of 1.3% on average.

Foundation Support

国家自然科学基金资助项目(61671001)
国家重点研发计划资助项目(2016YFB0800101,2017YFB0803204)
鹏城实验室资助项目(PCL2018KP001)
广东省重点领域研发计划资助项目(2019B010137001)
深圳市基础研究课题(JCYJ20170306092030521)
中国博士后科学基金资助项目(2020TQ0158,2020M682825)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.09.0625
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 1
Section: Technology of Network & Communication
Pages: 237-240,255
Serial Number: 1001-3695(2021)01-047-0237-04

Publish History

[2021-01-05] Printed Article

Cite This Article

李文军, 刘馨蔚, 邢凯轩, 等. 基于决策树映射的低功耗TCAM包分类方案 [J]. 计算机应用研究, 2021, 38 (1): 237-240,255. (Li Wenjun, Liu Xinwei, Xing Kaixuan, et al. Decision tree based pre-classifier for energy-efficient TCAM based packet classification [J]. Application Research of Computers, 2021, 38 (1): 237-240,255. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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