A Hybrid Forwarding Information Base for Multi-Modal Data

In order to solve the problems of rapid indexing, efficient storage of forwarding information and longest prefix matching brought by multi-modal data in the forwarding information base(FIB) in the future network, a hybrid FIB based on neural networks, called Hybrid-FIB, which supports multi-modal data indexing is designed. Hybrid-FIB differentiates different type of data to obtain input vectors for neural network model, and then trains a neural network hybrid index model that can achieve uniform distribution. Experiments show that deploying two sets of Hybrid-FIB on the static random access memory can not only achieve the longest prefix matching of the multi-modal data, but also have better retrieval speed and misjudgment rate than the current network. © 2020, Editorial Department of Journal of Beijing University of Posts and Telecommunications. All right reserved.