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 Electrical Engineering Research Papers |
Files in This Item:
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Fulltext.pdf | 8.04 MB | Adobe PDF | View/Open |
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