• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.43 no.5 s.347
• /
• pp.137-146
• /
• 2006

IP address lookup has become a major bottleneck of packet forwarding and a critical issue for high-speed networking techniques in routers. In this paper, we propose an efficient high-speed IP address lookup scheme using adaptive multiple hashing and prefix grouping. According to our analysis results based on routing data distributions, we grouped prefix lengths and selected the number of hash functions in each group adaptively. As a result, we can reduce collisions caused by hashing. Accordingly, a forwarding table of our scheme has good memory efficiency, and thus is organized with the proper number of memory modules. Also, the proposed scheme has the fast building and searching mechanisms to develop the forwarding table only during a single memory access.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.7
• /
• pp.2599-2613
• /
• 2015

Multi-index hashing (MIH) is the state-of-the-art method for indexing binary codes, as it di-vides long codes into substrings and builds multiple hash tables. However, MIH is based on the dataset codes uniform distribution assumption, and will lose efficiency in dealing with non-uniformly distributed codes. Besides, there are lots of results sharing the same Hamming distance to a query, which makes the distance measure ambiguous. In this paper, we propose a data-oriented multi-index hashing method (DOMIH). We first compute the covariance ma-trix of bits and learn adaptive projection vector for each binary substring. Instead of using substrings as direct indices into hash tables, we project them with corresponding projection vectors to generate new indices. With adaptive projection, the indices in each hash table are near uniformly distributed. Then with covariance matrix, we propose a ranking method for the binary codes. By assigning different bit-level weights to different bits, the returned bina-ry codes are ranked at a finer-grained binary code level. Experiments conducted on reference large scale datasets show that compared to MIH the time performance of DOMIH can be improved by 36.9%-87.4%, and the search accuracy can be improved by 22.2%. To pinpoint the potential of DOMIH, we further use near-duplicate image retrieval as examples to show the applications and the good performance of our method.