Knowledge-Defined Networking

TL;DR

This paper introduces Knowledge-Defined Networking (KDN), a new paradigm combining SDN, Network Analytics, and AI to improve network control and operation, supported by use cases and preliminary experimental results.

Contribution

It proposes the KDN paradigm that integrates SDN, NA, and AI, addressing previous deployment challenges and demonstrating feasibility through initial experiments.

Findings

KDN leverages SDN and NA to enhance network management.

Preliminary experiments support the feasibility of KDN.

Use cases illustrate potential benefits of the paradigm.

Abstract

The research community has considered in the past the application of Artificial Intelligence (AI) techniques to control and operate networks. A notable example is the Knowledge Plane proposed by D.Clark et al. However, such techniques have not been extensively prototyped or deployed in the field yet. In this paper, we explore the reasons for the lack of adoption and posit that the rise of two recent paradigms: Software-Defined Networking (SDN) and Network Analytics (NA), will facilitate the adoption of AI techniques in the context of network operation and control. We describe a new paradigm that accommodates and exploits SDN, NA and AI, and provide use cases that illustrate its applicability and benefits. We also present simple experimental results that support its feasibility. We refer to this new paradigm as Knowledge-Defined Networking (KDN).