Mostra el registre d'ítem simple
ENERO: Efficient real-time WAN routing optimization with Deep Reinforcement Learning
dc.contributor.author | Almasan Puscas, Felician Paul |
dc.contributor.author | Xiao, Shihan |
dc.contributor.author | Cheng, Xiangle |
dc.contributor.author | Shi, Xiang |
dc.contributor.author | Barlet Ros, Pere |
dc.contributor.author | Cabellos Aparicio, Alberto |
dc.contributor.other | Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors |
dc.date.accessioned | 2022-10-13T10:23:13Z |
dc.date.available | 2022-10-13T10:23:13Z |
dc.date.issued | 2022-09-04 |
dc.identifier.citation | Almasan, P. [et al.]. ENERO: Efficient real-time WAN routing optimization with Deep Reinforcement Learning. "Computer networks", 4 Setembre 2022, vol. 214, article 109166, p. 1-11. |
dc.identifier.issn | 1389-1286 |
dc.identifier.uri | http://hdl.handle.net/2117/374335 |
dc.description.abstract | Wide Area Networks (WAN) are a key infrastructure in today’s society. During the last years, WANs have seen a considerable increase in network’s traffic and network applications, imposing new requirements on existing network technologies (e.g., low latency and high throughput). Consequently, Internet Service Providers (ISP) are under pressure to ensure the customer’s Quality of Service and fulfill Service Level Agreements. Network operators leverage Traffic Engineering (TE) techniques to efficiently manage the network’s resources. However, WAN’s traffic can drastically change during time and the connectivity can be affected due to external factors (e.g., link failures). Therefore, TE solutions must be able to adapt to dynamic scenarios in real-time. In this paper we propose Enero, an efficient real-time TE solution based on a two-stage optimization process. In the first one, Enero leverages Deep Reinforcement Learning (DRL) to optimize the routing configuration by generating a long-term TE strategy. To enable efficient operation over dynamic network scenarios (e.g., when link failures occur), we integrated a Graph Neural Network into the DRL agent. In the second stage, Enero uses a Local Search algorithm to improve DRL’s solution without adding computational overhead to the optimization process. The experimental results indicate that Enero is able to operate in real-world dynamic network topologies in 4.5 s on average for topologies up to 100 links. |
dc.description.sponsorship | This publication is part of the Spanish I+D+i project TRAINER-A (ref.PID2020-118011GB-C21), funded by MCIN/ AEI/ 10.13039/501100011033. This work is also partially funded by the Catalan Institution for Research and Advanced Studies (ICREA) and the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund. |
dc.format.extent | 11 p. |
dc.language.iso | eng |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
dc.subject | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors |
dc.subject.lcsh | Telecommunication -- Traffic -- Management |
dc.subject.lcsh | Routing (Computer network management) |
dc.subject.lcsh | Neural networks (Computer science) |
dc.subject.lcsh | Deep learning |
dc.subject.other | Optimization |
dc.subject.other | Deep reinforcement learning |
dc.subject.other | Graph neural networks |
dc.title | ENERO: Efficient real-time WAN routing optimization with Deep Reinforcement Learning |
dc.type | Article |
dc.subject.lemac | Telecomunicació -- Tràfic -- Gestió |
dc.subject.lemac | Encaminament (Gestió de xarxes d'ordinadors) |
dc.subject.lemac | Xarxes neuronals (Informàtica) |
dc.subject.lemac | Aprenentatge profund |
dc.identifier.doi | 10.1016/j.comnet.2022.109166 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S1389128622002717 |
dc.rights.access | Open Access |
local.identifier.drac | 34288070 |
dc.description.version | Postprint (published version) |
dc.relation.projectid | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-118011GB-C21/ES/INVESTIGACION EN FUTURAS REDES TOTALMENTE OPTIMIZADAS MEDIANTE INTELIGENCIA ARTIFICIAL - A/ |
local.citation.author | Almasan, P.; Xiao, S.; Cheng, X.; Shi, X.; Barlet, P.; Cabellos-Aparicio, A. |
local.citation.publicationName | Computer networks |
local.citation.volume | 214 |
local.citation.number | article 109166 |
local.citation.startingPage | 1 |
local.citation.endingPage | 11 |
Fitxers d'aquest items
Aquest ítem apareix a les col·leccions següents
-
Articles de revista [1.050]
-
Articles de revista [164]