Intelligent Reinforcement-learning-based Network Management
draft-kim-nmrg-rl-05

Document Type Expired Internet-Draft (individual)
Authors Min-Suk Kim  , Youn-Hee Han  , Yong-Geun Hong 
Last updated 2020-01-09 (latest revision 2019-07-08)
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This Internet-Draft is no longer active. A copy of the expired Internet-Draft can be found at
https://www.ietf.org/archive/id/draft-kim-nmrg-rl-05.txt

Abstract

This document presents intelligent network management based on Artificial Intelligent (AI) such as reinforcement-learning approaches. In a heterogeneous network, intelligent management with Artificial Intelligent should usually provide real-time connectivity, the type of network management with the quality of real-time data, and transmission services generated by an application service. With that reason intelligent management system is needed to support real- time connection and protection through efficient management of interfering network traffic for high-quality network data transmission in the both cloud and IoE network systems. Reinforcement-learning is one of the machine learning algorithms that can intelligently and autonomously provide to management systems over a communication network. Reinforcement-learning has developed and expanded with deep learning technique based on model-driven or data- driven technical approaches so that these trendy techniques have been widely to intelligently attempt an adaptive networking models with effective strategies in environmental disturbances over variety of networking areas. For Network AI with the intelligent and effective strategies, intent-based network (IBN) can be also considered to continuously and automatically evaluate network status under required policy for dynamic network optimization. The key element for the intent-based network is that it provides a verification of whether the represented network intent is implementable or currently implemented in the network. Additionally, this approach need to provide to take action in real time if the desired network state and actual state are inconsistent.

Authors

Min-Suk Kim (mskim16@etri.re.kr)
Youn-Hee Han (yhhan@koreatech.ac.kr)
Yong-Geun Hong (YGHONG@ETRI.RE.KR)

(Note: The e-mail addresses provided for the authors of this Internet-Draft may no longer be valid.)