ADAFT：SDN大规模流表的适应性深度聚合存储架构

doi:10.11959/j.issn.1000-436x.2024059

通信学报 ›› 2024, Vol. 45 ›› Issue (5): 226-238.doi: 10.11959/j.issn.1000-436x.2024059

• 学术通信 • 上一篇

ADAFT：SDN大规模流表的适应性深度聚合存储架构

熊兵¹, 袁月¹, 赵锦元², 赵宝康³(), 何施茗¹, 张锦¹

^1.长沙理工大学计算机与通信工程学院, 湖南长沙 410114
^2.长沙师范学院信息科学与工程学院, 湖南长沙 410199
^3.国防科技大学计算机学院, 湖南长沙 410073

收稿日期:2023-10-12 修回日期:2024-02-19 出版日期:2024-05-30 发布日期:2024-06-24
通讯作者: 赵宝康 E-mail:bkzhao@nudt.edu.cn
作者简介:熊兵（1981- ），男，湖南益阳人，博士，长沙理工大学副教授，主要研究方向为未来网络、网络测量、网络安全等。
赵锦元（1980- ），女，湖南邵阳人，博士，长沙师范学院副教授，主要研究方向为未来网络、网络测量、网络建模与优化等。
赵宝康（1981- ），男，湖北天门人，博士，国防科技大学副教授，主要研究方向为网络体系结构与协议、卫星互联网、高性能网络、网络安全等。
何施茗（1986- ），女，湖南永州人，博士，长沙理工大学副教授，主要研究方向为异常检测、矩阵分解、图网络与数据处理、无线网络。
张锦（1979- ），男，河南信阳人，博士，长沙理工大学教授，主要研究方向为人工智能、软件工程。
基金资助:
国家自然科学基金资助项目(U22B2005);国家重点研发计划基金资助项目(2022YFB2901204);湖南省自然科学基金资助项目(2023JJ30053);湖南省教育厅基金资助项目(22A0232);湖南省研究生科研创新基金资助项目(CX20230913)

ADAFT：an storage architecture of large-scale SDN flow tables based on adaptive deep aggregations

Bing XIONG¹, Yue YUAN¹, Jinyuan ZHAO², Baokang ZHAO³(), Shiming HE¹, Jin ZHANG¹

^1.School of Computer Science and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China
^2.School of Information Science and Engineering, Changsha Normal University, Changsha 410199, China
^3.School of Computer Science, National University of Defense Technology, Changsha 410073, China

Received:2023-10-12 Revised:2024-02-19 Online:2024-05-30 Published:2024-06-24
Contact: Baokang ZHAO E-mail:bkzhao@nudt.edu.cn
Supported by:
The National Natural Science Foundation of China(U22B2005);The National Key Research and Development Program of China(2022YFB2901204);The Natural Science Foundation of Hunan Province(2023JJ30053);Scientific Research Fund of Hunan Provincial Education Department(22A0232);The Postgraduate Scientific Research Innovation Project of Hunan Province(CX20230913)

摘要/Abstract

摘要：

为解决软件定义网络（SDN）数据平面中的三态内容可寻址存储器（TCAM）资源紧张问题，提出了一种基于内容表项树的SDN流表深度聚合方法，进而构建一种SDN大规模流表的适应性深度聚合存储架构ADAFT。该架构放宽了聚合表项之间的汉明距离要求，构建内容表项树聚合动作集不同的流表项，显著提高了流表聚合程度。设计了一种TCAM装载率感知的内容表项树动态限高机制，以降低流表查找开销。同时，提出了一种TCAM装载率感知的表项聚合适应性选择策略，以均衡流表聚合程度和查找开销。实验结果表明，ADAFT架构的流表压缩率明显高于现有方法，最高可达65.74%。

关键词: 软件定义网络, SDN大规模流表, 内容表项树, 适应性深度聚合, TCAM装载率感知

Abstract:

To solve the problem of resource shortage of ternary content addressable memory (TCAM) in the data plane of software defined network (SDN), a deep flow table aggregation method was proposed based on content entry trees, and a storage architecture of large-scale SDN flow tables named ADAFT was established. The architecture relaxed the Hamming distance requirement between ag-gregated flow entries, and a content entry tree was constructed to aggregate flow entries with different action sets, for significantly en-hancing the aggregation degree of flow tables. Then a dynamic limitation mechanism was designed for the height of content entry trees based on the awareness of TCAM load ratio, to minimize the lookup overhead of aggregated flow tables. Meanwhile, an adaptive selec-tion strategy of flow entry aggregation was presented in the light of TCAM load ratio, to strike a balance between the aggregation degree and lookup overhead of flow tables. Experimental results indicate that the ADAFT architecture achieves much higher flow table com-pression ratios up to 65.74% than existing methods.

Key words: software defined network, large-scale SDN flow table, content entry tree, adaptive deep aggregation, TCAM load ratio awareness

中图分类号:

TP393

熊兵, 袁月, 赵锦元, 赵宝康, 何施茗, 张锦. ADAFT：SDN大规模流表的适应性深度聚合存储架构[J]. 通信学报, 2024, 45(5): 226-238.

Bing XIONG, Yue YUAN, Jinyuan ZHAO, Baokang ZHAO, Shiming HE, Jin ZHANG. ADAFT：an storage architecture of large-scale SDN flow tables based on adaptive deep aggregations[J]. Journal on Communications, 2024, 45(5): 226-238.

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ADAFT：SDN大规模流表的适应性深度聚合存储架构

ADAFT：an storage architecture of large-scale SDN flow tables based on adaptive deep aggregations

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