Network Assurance in Intent-Based Networking Data Centers with Machine Learning Techniques
Xiaoang Zheng, Aris Leivadeas
Abstract
Intent-Based Networking (IBN) is a recently proposed networking solution that allows networks to be configured and adapted autonomously according to the users' or operators' high-level intentions. However, a significant component of IBN is to assure that the network accurately and automatically deploys the intent throughout its lifecycle. To this end, in this study, we propose a network assurance solution for data center IBN networks. For the assurance model, we propose some specific data preparation procedures and Machine Learning (ML) models for the problem of time series forecasting. Specifically, we construct three main ML models that are based on the architecture of Convolutional Neural Networks (CNN) and Recurrent Neural Network (RNN). Our evaluation experiments, based on real data center Virtual Machine (VM) data traces, reveal the effectiveness of our methods in terms of CPU percentage usage prediction accuracy and speed. At the same time, our best-performing model can predict sufficiently far into the future with good accuracy.