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

作　　者：熊兵[1] 袁月 赵锦元 赵宝康[3] 何施茗[1] 张锦 XIONG Bing;YUAN Yue;ZHAO Jinyuan;ZHAO Baokang;HE Shiming;ZHANG Jin(School of Computer Science and Communication Engineering,Changsha University of Science and Technology,Changsha 410114,China;School of Information Science and Engineering,Changsha Normal University,Changsha 410199,China;School of Computer Science,National University of Defense Technology,Changsha 410073,China)

机构地区：[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

出　　处：《通信学报》2024年第5期226-238,共13页Journal on Communications

基　　金：国家自然科学基金资助项目(No.U22B2005,No.61972412,No.62272062);国家重点研发计划基金资助项目(No.2022YFB2901204);湖南省自然科学基金资助项目(No.2023JJ30053,No.2021JJ30456);湖南省教育厅基金资助项目(No.22A0232,No.23A0735,No.22B0300);湖南省研究生科研创新基金资助项目(No.CX20230913)。

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%。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 Ham‐ming 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 limi‐tation mechanism was designed for the height of content entry trees based on the awareness of TCAM load ratio,to mini‐mize the lookup overhead of aggregated flow tables.Meanwhile,an adaptive selec-tion strategy of flow entry aggrega‐tion was presented in the light of TCAM load ratio,to strike a balance between the aggregation degree and lookup over‐head 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：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 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]