Enhancing Routing in SD-EONs through Reinforcement Learning: A Comparative Analysis
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| Format: | Preprint |
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2024
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| _version_ | 1866929549520928768 |
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| author | McCann, Ryan Rezaee, Arash Vokkarane, Vinod M. |
| author_facet | McCann, Ryan Rezaee, Arash Vokkarane, Vinod M. |
| contents | This paper presents an optimization framework for routing in software-defined elastic optical networks using reinforcement learning algorithms. We specifically implement and compare the epsilon-greedy bandit, upper confidence bound (UCB) bandit, and Q-learning algorithms to traditional methods such as K-Shortest Paths with First-Fit core and spectrum assignment (KSP-FF) and Shortest Path with First-Fit (SPF-FF) algorithms. Our results show that Q-learning significantly outperforms traditional methods, achieving a reduction in blocking probability (BP) of up to 58.8% over KSP-FF, and 81.9% over SPF-FF under lower traffic volumes. For higher traffic volumes, Q-learning maintains superior performance with BP reductions of 41.9% over KSP-FF and 70.1% over SPF-FF. These findings demonstrate the efficacy of reinforcement learning in enhancing network performance and resource utilization in dynamic and complex environments. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_13972 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Routing in SD-EONs through Reinforcement Learning: A Comparative Analysis McCann, Ryan Rezaee, Arash Vokkarane, Vinod M. Networking and Internet Architecture This paper presents an optimization framework for routing in software-defined elastic optical networks using reinforcement learning algorithms. We specifically implement and compare the epsilon-greedy bandit, upper confidence bound (UCB) bandit, and Q-learning algorithms to traditional methods such as K-Shortest Paths with First-Fit core and spectrum assignment (KSP-FF) and Shortest Path with First-Fit (SPF-FF) algorithms. Our results show that Q-learning significantly outperforms traditional methods, achieving a reduction in blocking probability (BP) of up to 58.8% over KSP-FF, and 81.9% over SPF-FF under lower traffic volumes. For higher traffic volumes, Q-learning maintains superior performance with BP reductions of 41.9% over KSP-FF and 70.1% over SPF-FF. These findings demonstrate the efficacy of reinforcement learning in enhancing network performance and resource utilization in dynamic and complex environments. |
| title | Enhancing Routing in SD-EONs through Reinforcement Learning: A Comparative Analysis |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2410.13972 |