在新兴的软件定义网络(Software Defined Networking,SDN)、OpenFlow交换机中,为满足OpenFlow协议宽匹配域的需求,SDN交换设备需要更大的查找表存储容量.当流表溢出时,将导致控制报文数目爆炸性增长、数据包传输时延增大等危害网络正常运行的后果.然而考虑成本因素,高速查找表容量不可能无限增加.即使单纯地增加流表容量,并不能使溢出的概率降低为零,且极不经济.本文分析了网络流量的特征,提出了一种流表共享方法(Flow Table Sharing,FTS),针对流表溢出现象带来的危害,完善了Table-Miss处理机制,有效遏制了由于流表溢出而引发的危害网络正常运行的情况.相比目前的Table-Miss处理方式,FTS对流表溢出情况下控制消息数量和RTT时间的优化都达到两个数量级.此外,该文针对流表扩散方法设计了简单高效的基于OpenFlow组表的随机路由选择算法,系统结构实施简单,可以方便地降级为现行的通用Table-Miss处理模式.
The scalability of routing architectures for large networks is one of the biggest challenges that the Internet faces today.Greedy routing,in which each node is assigned a locator used as a distance metric,recently received increased attention from researchers and is considered as a potential solution for scalable routing.In this paper,LMD—a local minimum driven method is proposed to compute the topology-based locator.To eliminate the negative effect of the " quasi" greedy property—transfer routes longer than the shortest routes,a two-stage routing strategy is introduced,which combines the greedy routing with source routing.The greedy routing path discovered and compressed in the first stage is then used by the following source-routing stage.Through extensive evaluations,based on synthetic topologies as well as on a snapshot of the real Internet AS(autonomous system)topology,it is shown that LMD guarantees 100%delivery rate on large networks with low stretch.
