Learned Bloom-filter for the efficient name lookup in Information-Centric Networking

Abstract Information-Centric Networking (ICN) uses content name to replace traditional IP address where data becomes independent from location, application, storage, and means of transportation. Due to the complex structure and variable length of content names, designing efficient content name lookup algorithms becomes a new challenge. In this paper, 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 to reduce the false negatives generated by the learned model. In addition, we evaluate the performance of the proposed algorithm using experimental simulations. Compared with the Bloom-Hash and NameFilter methods, the results show that our method can reduce the false positive rate and improve the accuracy of the search. Furthermore, the memory required by our method is less than the Bloom-Hash and NameFilter methods.