Learned Bloom-Filter for an Efficient Name Lookup in Information-Centric Networking

The information name replaces traditional IP address as the identity of the network transmission is a typical feature of Information-Centric Networking (ICN). Therefore, designing efficient lookup algorithms of information names becomes a new challenge. For this reason, we propose an efficient name lookup structure for ICN, called Learned Bloom-Filter Lookup, which combines Recurrent Neural Networks (RNN) with standard Bloom filter to improve lookup efficiency. In our scheme, RNN trains the element set and non-element set, which are used to obtain the pre-filtering of names. Moreover, we look up the contents by using the backup Bloom filter, which can improve the accuracy of the search. In addition, we evaluate the performance of the proposed algorithm by experimental simulations. Compared with the Bloom-Hash method, our results show that our method can reduce the false positive rate. Furthermore, the memory required by our method is less than the Bloom-Hash method.