A Pareto-Optimality Based Multi-Objective Optimisation Approach to Assist Optical Network (Re-)Design Choices

Nallaperuma, Sam; Shevchenko, Nikita A.; Savory, Seb J.

doi:10.1109/ecoc52684.2021.9605982

Cited by 1 publication

(2 citation statements)

References 20 publications

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“…In general, a multi-objective optimization problem (MOP) considers multiple objective functions simultaneously and the Pareto-optimality of the solutions is defined as the set of nondominated solutions, where dominance relation is formulated as follows [28]…”

Section: Multi-objective Reinforcement Learning Framework For Paramet...mentioning

confidence: 99%

“…To the best of our knowledge, this is the first study to tackle this challenging problem, which involves optimizing network throughput, cost, and resilience simultaneously. Traditional meta-heuristic [21][22][23] and exact [24] multi-objective optimization approaches are not practical in this setting due to their high running times, which make them impractical for online servicing of new traffic requests. In contrast, our approach utilizes RL to rapidly provide optimal RWA solutions, making it a practical and efficient solution for this problem.…”

Section: Introductionmentioning

confidence: 99%

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Interpreting multi-objective reinforcement learning for routing and wavelength assignment in optical networks

Nallaperuma¹,

Gan²,

Nevin³

et al. 2023

J. Opt. Commun. Netw.

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Performance optimization literature in optical networks predominantly consists of single objective optimization studies while often in practice multiple performance goals are to be met. This study addresses this issue with a generalized reinforcement learning (RL) model for parameter optimization in optical networks in the presence of multiple performance goals. Using this generic model, two multi-objective variants of a classical optimization problem in optical network operation, routing and wavelength assignment (RWA), are derived and solved to near optimality. The allocated route and wavelength for each demand are optimized with respect to the number of accepted services, the number of transmitters, and network availability. The resultant approximated Pareto front provides a set of solutions from which network operators can make decisions based on their preferences for particular objectives. These results contribute to the understanding of the relationships between different network parameters and performance metrics, which would be beneficial in future network design and growth. Moreover, benchmarking results against the state-of-the-art RWA heuristics suggest the applicability of RL in dynamic settings under changing traffic and generalizability for unseen traffic.

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Section: Multi-objective Reinforcement Learning Framework For Paramet...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Interpreting multi-objective reinforcement learning for routing and wavelength assignment in optical networks

Nallaperuma¹,

Gan²,

Nevin³

et al. 2023

J. Opt. Commun. Netw.

Self Cite

View full text Add to dashboard Cite

show abstract

A Pareto-Optimality Based Multi-Objective Optimisation Approach to Assist Optical Network (Re-)Design Choices

Cited by 1 publication

References 20 publications

Interpreting multi-objective reinforcement learning for routing and wavelength assignment in optical networks

Interpreting multi-objective reinforcement learning for routing and wavelength assignment in optical networks

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