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

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.