命名数据网络中一种基于节点分类的数据存储策略

摘要

Compared with the traditional Internet , in‐networking caching is one of the most distinguishable features in named data networking (NDN) .In NDN ,a node caches every passing data packet as a default model . The caching scheme generates a large number of redundant data in in‐networking .As a consequence , the networking cache resource is wasted seriously . To solve the problem ,a caching scheme based on node classification (BNC) is proposed firstly in this paper .Based on different node positions ,the nodes that data packet passes through are divided into two types :“edge” type and “core” type .When data packet passes through the “core” type nodes ,by considering location and data popularity distribution at different nodes ,it is cached in a node which is beneficial to other nodes .When the data packet passes through the “edge” nodes ,a node is selected through data popularity to be beneficial to the client .The simulation results show that the proposed scheme can efficiently improve the in‐network hit ratio and reduce the delay and hops of getting the data packet .%缓存是命名数据网络（named data networking ，NDN ）有别于传统网络最突出的特性之一， NDN 中默认所有节点都具有缓存所有经过数据的功能。这种“处处缓存”策略导致网内大量冗余数据的产生，使网内缓存被严重浪费。针对上述问题，首次提出了一种基于节点分类（based on node classification ，BNC）的数据存储策略。基于节点位置的不同，将数据返回客户端所经过的节点分为“边缘”类节点与“核心”类节点。当数据经过“核心”类节点时，通过权衡该类节点的位置与数据在不同节点的流行度分布，将数据存储在对其他节点最有利的节点中；当数据经过“边缘”类节点时，通过该数据流行度来选择最有利于客户端的位置。仿真结果表明，提出的策略将有效提高数据命中率，减少数据请求时延和距离。