An Intelligence-Defined Networking Architecture With Importance-Based Network Resource Control

Abstract

As network complexity increases because of the diversification of applications and services based on Internet of Autonomous Things (IoAT), it is difficult for humans to design the optimal control rule for software-defined networking (SDN) controllers. Intelligence-defined networking, called IDN, is proposed to overcome this limitation through machine learning (ML) algorithms. Since the existing IDN approaches are mostly designed to optimize only the network Quality of Services (QoS), including throughput, jitter, and latency, the controllers do not consider the importance of data in the applications and the services. This causes the controller to allocate insufficient resources to the crucial data flow, which leads critical problems, such as self-driving car accidents. To prevent this problem, we propose an importance-based IDN architecture that enables network controllers to manage network traffic with an importance levels (ILs) of data flows. First, we devise an importance estimation scheme to set the IL for the flows. Second, a dynamic resource allocation model of the controllers is developed by means of deep learning algorithms in order to make the optimal network resource. Additionally, an online learning mechanism based on weighted auto-labeling is adopted to continue enhancing the adaptability of the resource allocation model on runtime as the network conditions change. The evaluation results of the proposed architecture under various autonomous things scenarios show that the loss rate for data flows of higher importance is reduced by one-quarter compared to the case of a network controller without IL and that the bandwidth waste ratio is reduced by 10% compared to the rule-based model.