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
- 通信学报 2024年45卷第5期 页码：226-238
- 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：
[ "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。" ]
- 基金信息：国家自然科学基金资助项目(U22B2005;61972412;62272062);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;2021JJ30456);湖南省教育厅基金资助项目(22A0232;23A0735;22B0300);湖南省研究生科研创新基金资助项目(CX20230913)
DOI：10.11959/j.issn.1000-436x.2024059
中图分类号： TP393收稿：2023-10-12，
修回：2024-02-19，
纸质出版：2024-05-30
- 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.
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references
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