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Descrição Detalhada da Publicação
A Bayesian multi-armed bandit algorithm for dynamic end-to-end routing in SDN-based networks with piecewise-stationary rewards
Título Revista
Algorithms
Ano (publicação definitiva)
2023
Língua
Inglês
País
Suíça
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Abstract/Resumo
To handle the exponential growth of data-intensive network edge services and automatically solve new challenges in routing management, machine learning is steadily being incorporated into software-defined networking solutions. In this line, the article presents the design of a piecewise-stationary Bayesian multi-armed bandit approach for the online optimum end-to-end dynamic routing of data flows in the context of programmable networking systems. This learning-based approach has been analyzed with simulated and emulated data, showing the proposal’s ability to sequentially and proactively self-discover the end-to-end routing path with minimal delay among a considerable number of alternatives, even when facing abrupt changes in transmission delay distributions due to both variable congestion levels on path network devices and dynamic delays to transmission links.
Agradecimentos/Acknowledgements
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Palavras-chave
Networks,Routing,Congestion,Variable link delay,SDN,Algorithm design,Multi-armed bandits
Classificação Fields of Science and Technology
- Matemáticas - Ciências Naturais
- Ciências da Computação e da Informação - Ciências Naturais
Registos de financiamentos
Referência de financiamento | Entidade Financiadora |
---|---|
UIDB/04466/2020 | Fundação para a Ciência e a Tecnologia |
UIDB/50008/2020 | Fundação para a Ciência e a Tecnologia |
UIDP/04466/2020 | Fundação para a Ciência e a Tecnologia |
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