# Knowledge-Defined Networking

arXiv: https://arxiv.org/abs/1606.06222 · HTML: https://mlsanity.com/paper/1606.06222/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2016.

## Built on by (most cited first, 9 of 9)

- [netFound: Principled Design for Network Foundation Models](https://mlsanity.com/paper/2310.17025/) (2023)
- [MAGNNETO: A Graph Neural Network-based Multi-Agent system for Traffic Engineering](https://mlsanity.com/paper/2303.18157/) (2023)
- [FENXI: Deep-learning Traffic Analytics at the Edge](https://mlsanity.com/paper/2105.11738/) (2021)
- [Learning Sub-Second Routing Optimization in Computer Networks requires Packet-Level Dynamics](https://mlsanity.com/paper/2410.10377/) (2024)
- [NetBurst: Event-Centric Forecasting of Bursty, Intermittent Time Series](https://mlsanity.com/paper/2510.22397/) (2025)
- [KP-A: A Unified Network Knowledge Plane for Catalyzing Agentic Network Intelligence](https://mlsanity.com/paper/2507.08164/) (2025)
- [SeaNet -- Towards A Knowledge Graph Based Autonomic Management of Software Defined Networks](https://mlsanity.com/paper/2106.13367/) (2021)
- [Machine Learning Empowered Intelligent Data Center Networking: A Survey](https://mlsanity.com/paper/2202.13549/) (2022)
- [Towards Near-Real-Time Telemetry-Aware Routing with Neural Routing Algorithms](https://mlsanity.com/paper/2604.02927/) (2026)

## Abstract (arXiv)

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).