Knowledge-defined networking

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).Peer ReviewedPostprint (author's final draft

Machine Learning applied to the control of computer networks

En el projecte es defineix el Knowledge-Defined Networking, un paradigma de comunicació revolucionari creat entre la UPC, UC Berkeley i empreses com HP i CISCO que modela i optimitza la xarxa a través de l'aprenentatge automàtic. Se'n proposa una primera implementació i se'n discuteix la viabilitat.We define "Knowledge-Defined Networking", a revolutionary communication paradigm designed between UPC, UC Berkeley, HP, CISCO and other companies that optimizes and models the network throughout the use of machine learning. We propose a first implementation and discuss its feasibility

Machine Learning applied to the control of computer networks

En el projecte es defineix el Knowledge-Defined Networking, un paradigma de comunicació revolucionari creat entre la UPC, UC Berkeley i empreses com HP i CISCO que modela i optimitza la xarxa a través de l'aprenentatge automàtic. Se'n proposa una primera implementació i se'n discuteix la viabilitat.We define "Knowledge-Defined Networking", a revolutionary communication paradigm designed between UPC, UC Berkeley, HP, CISCO and other companies that optimizes and models the network throughout the use of machine learning. We propose a first implementation and discuss its feasibility

Knowledge-defined networking

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).Peer Reviewe