Traffic Engineering in WAN infrastructures is critical for the efficient management of costly resources and for guaranteeing acceptable QoS levels to applications. SD-WAN has recently emerged as a key solution to manage enterprise WANs, allowing fine-grained, policy-based control over traffic flows. In this paper, we propose a framework based on Reinforcement Learning for the effective use of multiple channels connecting distributed sites of a company. We evaluate it in a realistic, emulated network with a centralized SDN controller. Results show that under heavy load conditions, our approach leads to a 33% reduction in the number of QoS policy violations compared to a benchmark approach. Smaller average latency and connectivity costs are also obtained.

AI-enabled SD-WAN: the case of Reinforcement Learning / Botta, Alessio; Canonico, Roberto; Navarro, Annalisa; Ruggiero, Saverio; Ventre, Giorgio. - (2022), pp. 1-6. (Intervento presentato al convegno 2022 IEEE Latin-American Conference on Communications (LATINCOM) tenutosi a Rio de Janeiro, Brazil nel 30 November 2022 - 02 December 2022) [10.1109/LATINCOM56090.2022.10000667].

AI-enabled SD-WAN: the case of Reinforcement Learning

Botta, Alessio;Canonico, Roberto;Navarro, Annalisa;Ruggiero, Saverio;Ventre, Giorgio
2022

Abstract

Traffic Engineering in WAN infrastructures is critical for the efficient management of costly resources and for guaranteeing acceptable QoS levels to applications. SD-WAN has recently emerged as a key solution to manage enterprise WANs, allowing fine-grained, policy-based control over traffic flows. In this paper, we propose a framework based on Reinforcement Learning for the effective use of multiple channels connecting distributed sites of a company. We evaluate it in a realistic, emulated network with a centralized SDN controller. Results show that under heavy load conditions, our approach leads to a 33% reduction in the number of QoS policy violations compared to a benchmark approach. Smaller average latency and connectivity costs are also obtained.
2022
978-1-6654-8225-7
AI-enabled SD-WAN: the case of Reinforcement Learning / Botta, Alessio; Canonico, Roberto; Navarro, Annalisa; Ruggiero, Saverio; Ventre, Giorgio. - (2022), pp. 1-6. (Intervento presentato al convegno 2022 IEEE Latin-American Conference on Communications (LATINCOM) tenutosi a Rio de Janeiro, Brazil nel 30 November 2022 - 02 December 2022) [10.1109/LATINCOM56090.2022.10000667].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/913480
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