Link Traffic-Delay Mapping Model Learning Based on Multi-Class Samples in Software-Defined Networks

Delays are crucial factors in the service management of networks, especially software-defined networks. Unfortunately, it is very difficult to accurately model a traffic-delay mapping without any assumptions on an uncertain network. In this article, we present a machine learning-based solution to generate a mapping between link traffic and link delay in software-defined networks. The proposed solution only requires a small number of link delay samples from the production network. The small number of link delay samples is not sufficient for learning link traffic-delay mapping. To solve the above problem, we extend the link delay-related data via a sample transfer method and a distributed path delay data collection method without the assistance of the controller. We design a link traffic-delay mapping learning solution using the above three classes of data. This solution uses a traffic segment-based statistical mechanism to deduce the mean link delay effectively from the collected path delay information and implements effective sample transfer via a distance-based approximation. On the basis of specially designed deep learning structures and training procedures, the proposed learning solution effectively builds traffic-delay mapping models using the samples transferred from an experimental network and the samples of the production network.