dc.contributor.author | Zhongxia, Y | |
dc.contributor.author | Jingguo, G | |
dc.contributor.author | Wu, Y | |
dc.contributor.author | Liangxiong, L | |
dc.contributor.author | Tong, L | |
dc.date.accessioned | 2020-02-10T10:58:56Z | |
dc.date.issued | 2020-04-08 | |
dc.description.abstract | Virtual network embedding arranges virtual network services onto substrate network components. The performance of embedding algorithms determines the effectiveness and
efficiency of a virtualized network, making it a critical part of the
network virtualization technology. To achieve better performance,
the algorithm needs to automatically detect the network status
which is complicated and changes in a time-varying manner,
and to dynamically provide solutions that can best fit the current
network status. However, most existing algorithms fail to provide
automatic embedding solutions in an acceptable running time.
In this paper, we combine deep reinforcement learning with
a novel neural network structure based on graph convolutional networks, and propose a new and efficient algorithm for
automatic virtual network embedding. In addition, a parallel
reinforcement learning framework is used in training along
with a newly-designed multi-objective reward function, which
has proven beneficial to the proposed algorithm for automatic
embedding of virtual networks. Extensive simulation results
under different scenarios show that our algorithm achieves best
performance on most metrics compared with the existing stateof-the-art solutions, with upto 39.6% and 70.6% improvement
on acceptance ratio and average revenue, respectively. Moreover,
the results also demonstrate that the proposed solution possesses
good robustness. | en_GB |
dc.identifier.citation | Published online 08 April 2020. | en_GB |
dc.identifier.doi | 10.1109/JSAC.2020.2986662 | |
dc.identifier.uri | http://hdl.handle.net/10871/40799 | |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_GB |
dc.rights | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_GB |
dc.subject | Network Virtualization | en_GB |
dc.subject | Virtual Network Embedding | en_GB |
dc.subject | Reinforcement Learning | en_GB |
dc.subject | Graph Convolutional Network | en_GB |
dc.title | Automatic virtual network embedding: A deep reinforcement learning approach with graph convolutional networks | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2020-02-10T10:58:56Z | |
dc.identifier.issn | 0733-8716 | |
dc.description | This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record. | en_GB |
dc.identifier.journal | IEEE Journal on Selected Areas in Communications | en_GB |
dc.rights.uri | http://www.rioxx.net/licenses/all-rights-reserved | en_GB |
dcterms.dateAccepted | 2020-01-28 | |
rioxxterms.version | AM | en_GB |
rioxxterms.licenseref.startdate | 2020-01-28 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2020-02-10T10:37:49Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2020-04-09T10:08:02Z | |
refterms.panel | B | en_GB |