Knowledge-Defined Networking
arXiv:1606.06222 · doi:10.1145/3138808.3138810
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).
8 pages, 22 references, 6 figures and 1 table
Cited by in corpus (13)
- Survey on Multi-Access Edge Computing for Internet of Things Realization
- Deep Reinforcement Learning for Cyber Security
- RouteNet: Leveraging Graph Neural Networks for network modeling and optimization in SDN
- Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use case
- Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN
- An Exhaustive Survey on P4 Programmable Data Plane Switches: Taxonomy, Applications, Challenges, and Future Trends
- Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview
- Taurus: A Data Plane Architecture for Per-Packet ML
- Learn to Schedule (LEASCH): A Deep reinforcement learning approach for radio resource scheduling in the 5G MAC layer
- MAGNNETO: A Graph Neural Network-based Multi-Agent system for Traffic Engineering
- Message-Passing Neural Networks Learn Little's Law
- Review and Analysis of Recent Advances in Intelligent Network Softwarization for the Internet of Things
- DeepPlace: Learning to Place Applications in Multi-Tenant Clusters