Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/17416
Title: NetworkAI: An Intelligent Network Architecture for Self-Learning Control Strategies in Software Defined Networks
Authors: Yao, H
Mai, T
Xu, X
Zhang, P
Li, M
Liu, Y
Keywords: NetworkAI;Software Defined Networks;In-band Network Telemetry;Deep Reinforcement Learning
Issue Date: 24-Jul-2018
Publisher: IEEE
Citation: IEEE Internet of Things Journal, 2018
Abstract: The past few years have witnessed a wide deployment of software defined networks facilitating a separation of the control plane from the forwarding plane. However, the work on the control plane largely relies on a manual process in configuring forwarding strategies. To address this issue, this paper presents NetworkAI, an intelligent architecture for self-learning control strategies in SDN networks. NetworkAI employs deep reinforcement learning and incorporates network monitoring technologies such as the in-band network telemetry to dynamically generate control policies and produces a near optimal decision. Simulation results demonstrated the effectiveness of NetworkAI.
URI: http://bura.brunel.ac.uk/handle/2438/17416
DOI: http://dx.doi.org/10.1109/JIOT.2018.2859480
ISSN: http://dx.doi.org/10.1109/JIOT.2018.2859480
2327-4662
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

Files in This Item:
File Description SizeFormat 
Fulltext.pdf8.04 MBAdobe PDFView/Open


Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.