基于内容中心性的概率缓存内容放置方法

Probabilistic Caching Content Placement Method Based on Content-Centrality

为减少信息中心网络的缓存冗余,改善缓存命中率和利用率,提出了一种基于内容中心性的概率缓存内容放置方法(content-centrality-based probabilistic caching content placement method,CCPCP).与传统网络中仅用来刻画网络拓扑结构的中心性指标不同,采用的内容中心性指标,不仅能刻画缓存节点的位置中心属性,而且能刻画信息内容本身属性.该方法中,沿途各缓存节点综合考虑内容中心性和内容获取时延自适应地计算各自缓存概率,即内容所在节点位置越居于中心,内容热度越高,内容获取时延节省越优的内容被缓存的概率就越高.仿真实验表明:与现有基于概率缓存内容放置方法相比较,CCPCP方法缓存内容副本数目较少,减少率可达到32%以上,CCPCP方法显著地减少了缓存冗余,降低了内容获取时延,提高了缓存命中率和缓存内容利用率.

A content-centrality-based probabilistic caching content placement method(CCPCP)is proposed to reduce cache redundancy as well as achieve better performance in terms of cache hits and utilization in information-centric networking(ICN).Different from those metrics that focus only on the centrality based on topology in the traditional network,the content centrality metric is developed in this paper.The content centrality metric not only describes the location centrality of cache nodes,but also describes the attribute of the content.In CCPCP method,each cache node individually makes a cache decision with a certain caching probability.In particular,each cache node adaptively calculates the caching probability by jointly considering the content centrality and the delay savings,which is proportional to the location centrality,the content popularity and the access delay savings.That is,the larger location centrality of cache node,the higher popularity of content,the more access delay savings,and the larger the caching probability of cache node caching the passing content.Simulation results show that CCPCP outperforms the state-of-art probabilistic methods in terms of cache hit ratio,caching content utilization ratio,access delay and cache redundancy under the less number of content replicas,even in the case that the reduction of number of content replicas is up to 32%.