Intention Based Networking Management

Abstract

Compared to traditional networking which using command-line interfaces, intent-based networking abstracts network complexity and improves automation by eliminating manual configurations. It allows a user or administrator to send a simple request—using natural language—to plan, design and implement/operate the physical network which can improve network availability and agility. For example, an IT administrator can request improved voice quality for its voice-over-IP application, and the network can respond. For intent-based networking, the translation and validation system take a higher-level business policy (what) as input from end users and converts it to the necessary network configuration (how) by natural language understanding technology. In this chapter, we focus on how artificial intelligence technology can be used in the natural language understanding in translation and validation system. We firstly propose an effective model for the similarity metrics of English sentences. In the model, we first make use of word embedding and convolutional neural network (CNN) to produce a sentence vector and then leverage the information of the sentence vector pair to calculate the score of sentence similarity. Then, we propose the SM-CHI feature selection method based on the common method used in Chinese text classification. Besides, the improved CHI formula and synonym merging are used to select feature words so that the accuracy of classification can be improved and the feature dimension can be reduced. Finally, we present a novel approach which considers both the semantic and statistical information to improve the accuracy of text classification. The proposed approach computes semantic information based on HowNet and statistical information based on a kernel function with class-based weighting.