Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/17416
Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Yao, H | - |
dc.contributor.author | Mai, T | - |
dc.contributor.author | Xu, X | - |
dc.contributor.author | Zhang, P | - |
dc.contributor.author | Li, M | - |
dc.contributor.author | Liu, Y | - |
dc.date.accessioned | 2019-01-24T12:00:25Z | - |
dc.date.available | 2018-07-24 | - |
dc.date.available | 2019-01-24T12:00:25Z | - |
dc.date.issued | 2018-07-24 | - |
dc.identifier.citation | IEEE Internet of Things Journal, 2018 | en_US |
dc.identifier.issn | http://dx.doi.org/10.1109/JIOT.2018.2859480 | - |
dc.identifier.issn | 2327-4662 | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/17416 | - |
dc.description.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. | en_US |
dc.description.sponsorship | National Natural Science Foundation of China | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.subject | NetworkAI | en_US |
dc.subject | Software Defined Networks | en_US |
dc.subject | In-band Network Telemetry | en_US |
dc.subject | Deep Reinforcement Learning | en_US |
dc.title | NetworkAI: An Intelligent Network Architecture for Self-Learning Control Strategies in Software Defined Networks | en_US |
dc.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.1109/JIOT.2018.2859480 | - |
dc.relation.isPartOf | IEEE Internet of Things Journal | - |
pubs.publication-status | Accepted | - |
dc.identifier.eissn | 2327-4662 | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
Files in This Item:
File | Description | Size | Format | |
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Fulltext.pdf | 8.04 MB | Adobe PDF | View/Open |
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