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Conference Papers Year : 2024

Routing optimization based on DRL and Generative Adversarial Networks for SDN environments

Abstract

Traditional routing protocols and analytical routing optimization models face limitations in adapting to dynamic and complex environments such as SDN. Deep Reinforcement Learning (DRL) offers promise for addressing these challenges, but its intensive training phase hinders practical implementation. This paper presents a distributed DRL-based routing optimization solution in SDN, enhanced with Generative Adversarial Networks (GAN) to expedite agent training. Our approach, evaluated on a Containernet and OpenAI Gym-based testbed, effectively optimizes network traffic routes for diverse traffic classes, maximizing throughput. Activation of the GAN module significantly reduces training times, enhancing the feasibility of our solution for real-world deployment
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Dates and versions

hal-04549760 , version 1 (17-04-2024)

Identifiers

  • HAL Id : hal-04549760 , version 1

Cite

Juan Francisco Chafla Altamirano, Mariem Guitouni, Hassan Hassan, Khalil Drira. Routing optimization based on DRL and Generative Adversarial Networks for SDN environments. IEEE/IFIP Network Operations and Management Symposium, IEEE, May 2024, Seoul, South Korea. ⟨hal-04549760⟩
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