dc.contributor.author | Zuo, Y | |
dc.contributor.author | Wu, Y | |
dc.contributor.author | Min, G | |
dc.contributor.author | Cui, L | |
dc.date.accessioned | 2018-09-21T08:44:42Z | |
dc.date.issued | 2018-09-25 | |
dc.description.abstract | Recent advances in traffic engineering offer a series of techniques to address the network problems due to the explosive growth of Internet
traffic. In traffic engineering, dynamic path planning is essential for prevalent applications, e.g., load balancing, traffic monitoring and firewall.
Application-specific methods can indeed improve the network performance but can hardly be extended to general scenarios. Meanwhile, massive
data generated in the current Internet has not been fully exploited, which may convey much valuable knowledge and information to facilitate
traffic engineering. In this paper, we propose a learning-based network path planning method under forwarding constraints for finer-grained and
effective traffic engineering. We form the path planning problem as the problem of inferring a sequence of nodes in a network path and adapt a
sequence-to-sequence model to learn implicit forwarding paths based on empirical network traffic data. To boost the model performance, attention
mechanism and beam search are adapted to capture the essential sequential features of the nodes in a path and guarantee the path connectivity. To
validate the effectiveness of the derived model, we implement it in Mininet emulator environment and leverage the traffic data generated by both
a real-world GEANT network topology and a grid network topology to train and evaluate the model. Experiment results exhibit a high testing
accuracy and imply the superiority of our proposal. | en_GB |
dc.description.sponsorship | This work is partially supported by the UK EPSRC project
(Grant No.:EP/R030863/1) | en_GB |
dc.identifier.citation | Published online 25 September 2018 | en_GB |
dc.identifier.doi | 10.1016/j.future.2018.09.043 | |
dc.identifier.uri | http://hdl.handle.net/10871/34050 | |
dc.language.iso | en | en_GB |
dc.publisher | Elsevier | en_GB |
dc.rights | © 2018 The Authors. Published by Elsevier B.V. Open access. Under a Creative Commons license: https://creativecommons.org/licenses/by/4.0/ | en_GB |
dc.subject | Traffic Engineering | en_GB |
dc.subject | Path Planning | en_GB |
dc.subject | Deep Learning | en_GB |
dc.subject | Sequence-to-sequence | en_GB |
dc.title | Learning-based Network Path Planning for Traffic Engineering | en_GB |
dc.type | Article | en_GB |
dc.identifier.issn | 0167-739X | |
dc.description | This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record | en_GB |
dc.identifier.journal | Future Generation Computer Systems | en_GB |