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

XIONG Bing; YUAN Yue; ZHAO Jinyuan; ZHAO Baokang; HE Shiming; ZHANG Jin

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

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

Correspondences | 更新时间：2024-06-24

- ADAFT：an storage architecture of large-scale SDN flow tables based on adaptive deep aggregations
- Journal on Communications Vol. 45, Issue 5, Pages: 226-238(2024)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(U22B2005;61972412;62272062);The National Key Research and Development Program of China(2022YFB2901204);The Natural Science Foundation of Hunan Province(2023JJ30053;2021JJ30456);Scientific Research Fund of Hunan Provincial Education Department(22A0232;23A0735;22B0300);The Postgraduate Scientific Research Innovation Project of Hunan Province(CX20230913)
- DOI：10.11959/j.issn.1000-436x.2024059
  CLC： TP393
- Received：12 October 2023，
  
  Revised：2024-02-19，
  
  Published：30 May 2024
- 稿件说明：
移动端阅览
熊兵,袁月,赵锦元等.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(05):226-238.

XIONG Bing,YUAN Yue,ZHAO Jinyuan,et al.ADAFT：an storage architecture of large-scale SDN flow tables based on adaptive deep aggregations[J].Journal on Communications,2024,45(05):226-238.
熊兵,袁月,赵锦元等.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(05):226-238. DOI： 10.11959/j.issn.1000-436x.2024059.

XIONG Bing,YUAN Yue,ZHAO Jinyuan,et al.ADAFT：an storage architecture of large-scale SDN flow tables based on adaptive deep aggregations[J].Journal on Communications,2024,45(05):226-238. DOI： 10.11959/j.issn.1000-436x.2024059.

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%。

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.

关键词

Keywords

references

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