基于深度布隆过滤器的NDN网络三级名字查找方法

A Three-level Name Lookup Method Based on Deep Bloom Filter for Named Data Networking

为提高命名数据网络(Name Data Networking,NDN)路由过程中内容名字查找的效率,该文提出一种基于深度布隆过滤器的3级名字查找方法。该方法使用长短记忆神经网络(Long Short Term Memory,LSTM)与标准布隆过滤器相结合的方法优化名字查找过程;采用3级结构优化内容名字在内容存储器(Content Store,CS)、待定请求表(Pending Interest Table,PIT)中的精确查找过程,提高查找精度并降低内存消耗。从理论上分析了3级名字查找方法的假阳性率,并通过实验验证了该方法能够有效节省内存、降低查找过程的假阳性。

A three-level name lookup method based on deep Bloom filter is proposed to improve the searching efficiency of content name in the routing progress of the Named Data Networking(NDN).Firstly,in this method,the Long Short Term Memory(LSTM)is combined with standard Bloom filter to optimize the name searching progress.Secondly,a three-level structure is adopted to optimize the accurate content name lookup progresses in the Content Store(CS)and the Pending Interest Table(PIT)to promote lookup accuracy and reduce memory consumption.Finally,the error rate generated by content name searching method based on deep Bloom filter structure is analyzed in theory,and the experiment results prove that the proposed the threelevel lookup structure can compress memory and decrease the error effectively.