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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Intelligent Routing Based on Reinforcement Learning for Software-Defined Networking

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Author(s):
Casas-Velasco, Daniela M. [1] ; Rendon, Oscar Mauricio Caicedo [2] ; da Fonseca, Nelson L. S. [1]
Total Authors: 3
Affiliation:
[1] Univ Estadual Campinas, Inst Comp, BR-13083852 Campinas - Brazil
[2] Univ Cauca, Dept Telemat, Popayan 190002 - Colombia
Total Affiliations: 2
Document type: Journal article
Source: IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT; v. 18, n. 1, p. 870-881, MAR 2021.
Web of Science Citations: 1
Abstract

Traditional routing protocols employ limited information to make routing decisions, which can lead to a slow adaptation to traffic variability, as well as restricted support to the Quality of Service (QoS) requirements of applications. This article introduces a novel approach for routing in Software-defined networking (SDN), called Reinforcement Learning and Software-Defined Networking Intelligent Routing (RSIR). RSIR adds a Knowledge Plane to SDN and defines a routing algorithm based on Reinforcement Learning (RL) that takes into account link-state information to make routing decisions. This algorithm capitalizes on the interaction with the environment, the intelligence provided by RL and the global view and control of the network furnished by SDN, to compute and install, in advance, optimal routes in the forwarding devices. RSIR was extensively evaluated by emulation using real traffic matrices. Results show RSIR outperforms the Dijkstra's algorithm in relation to the stretch, link throughput, packet loss, and delay when available bandwidth, delay, and loss are considered individually or jointly for the computation of optimal paths. The results demonstrate that RSIR is an attractive solution for intelligent routing in SDN. (AU)

FAPESP's process: 15/24494-8 - Communications and processing of big data in cloud and fog computing
Grantee:Nelson Luis Saldanha da Fonseca
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 19/03268-0 - Software defined networking routing with machine learning
Grantee:Daniela Maria Casas Velasco
Support Opportunities: Scholarships in Brazil - Master