In-Network Intelligence Control: Toward a Self-Driving Networking Architecture

Abstract

The past few years have witnessed the compelling applications of the Internet of Things (IoT) in our daily life. Meanwhile, with the explosion of IoT devices and various applications, the expectations for the performance, reliability, and security of networks are greater than ever. The current end-host-based or centralized control framework incurs too much communication and computation overhead, therefore exhibiting tardiness and clumsiness in responding to network dynamics. Recently, with the advancement of programmable network hardware, it is possible to implement network functions inside the network. However, current in-network schemes are largely dependent on the manual process, which presents poor scalability and robustness. Therefore, in this article, we present a new intelligent network control architecture, in-network intelligence control. We design intelligent in-network devices that can automatically adapt to network dynamics by leveraging powerful machine learning adaptive abilities. In addition, to enhance the collaboration among distributed in-network devices, a centralized management plane is introduced to ease the training process of distributed switches. To demonstrate the technical feasibility and performance advantage of our architecture, we present three use cases: in-network load balance, in-network congestion control, and in-network DDoS detection.