Knowledge-Defined Networking
By A. Mestres, Alberto Rodríguez-Natal, Josep Carner, Pere Barlet-Ros, Eduard Alarcón, Marc Solé, Victor Muntés, D. Meyer, Sharon Barkai, Mike J. Hibbett and 10 more. Computer communication review, 2016
353 citations recorded by the source corpus.
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, for some relevant use-cases, its feasibility. We refer to this new paradigm as Knowledge-Defined Networking (KDN).
Subfields: Computer Networks and Communications, Hardware and Architecture
View Paper: https://www.semanticscholar.org/paper/f8e439c2ec02481303da70cd5187e224bcb770b9
How researchers see this paper
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In Computer Networks and Communications
- Nei Kato
- Fengxiao Tang
- Bomin Mao
- Zubair Muhammad Fadlullah
- Madhusanka Liyanage
- Pawani Porambage
- Mika E. Ylianttila
- Tao Huang
In Hardware and Architecture
Citations in the corpus
- RouteNet: Leveraging Graph Neural Networks for Network Modeling and Optimization in SDN (2019, background): “1 we show an architecture for network optimization within the context of the knowledge-Defined Networking (KDN) paradigm [1].”
- A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges (2019, background): “Learning from nodes that have only a small partial view of the complete system to perform control beyond the local domain is very complex [2].”
- State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems (2017, background): “, the so-called Knowledge Defined Networking [102], a joint consideration of Software Defined Networking (SDN), network analytics, and deep learning is essential.”
- Survey on Multi-Access Edge Computing for Internet of Things Realization (2018, background): “The exploitation of Knowledge-Defined Networking (KDN) to make intelligent predictions about offload costs can be leveraged for efficient resource allocation at MEC servers as well as the offloading decision making at IoT devices [209].”