命名数据网络下基于K-medoids的簇内Hash路由机制

K-medoids Based Intra-Cluster Hash Routing for Named Data Networking

命名数据网络(Named Data Networking,NDN)是以内容为中心的新型网络架构,其随处缓存策略存在缓存冗余过多、邻居缓存利用率低等问题,导致缓存空间的浪费及缓存效率的低下.本文提出的融合沿路径非协作和路径外协作的缓存路由机制(K-Medoids Hash Routing,KMHR),使用K-medoids算法选取层次簇内的中心点,并针对不同流行度的内容分别采用Hash路由及最短路径路由,保证簇内高流行度内容的精确定位和唯一性,降低缓存冗余,提高缓存效率.通过真实网络拓扑仿真得出,KMHR机制具有最低的请求时间、最优的路由增益和较少的缓存内容数量.

Named Data Networking（ NDN） is a newcontent-centric architecture. Its original Leave Copy Everywhere（ LCE） strategy has many shortcomings,for instance,massive cache redundancy and poor utilization of neighbors＇ cache,which waste cache space and lower cache efficiency. We proposed a routing scheme named K-M edoids Hash Routing（ KM-HR） combining on-path non-cooperation and off-path cooperation schemes. K-medoids algorithm is used to select some content routers as medoids in hierarchical clusters. And for different popularity of contents,we choose Hash routing or the shortest path routing separately. KM HR locates and insures the uniqueness of contents with high popularity in the cluster,which significantly reduces cache redundancy and improves cache efficiency. Based on the real world network topology,simulation results showthat KM HR has the shortest request time,optimal routing gain and fewer cached contents.