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Santana, P. & Moura, J. (2023). A Bayesian multi-armed bandit algorithm for dynamic end-to-end routing in SDN-based networks with piecewise-stationary rewards. Algorithms. 16 (5)
P. F. Santana and J. A. Moura, "A Bayesian multi-armed bandit algorithm for dynamic end-to-end routing in SDN-based networks with piecewise-stationary rewards", in Algorithms, vol. 16, no. 5, 2023
@article{santana2023_1730780236590, author = "Santana, P. and Moura, J.", title = "A Bayesian multi-armed bandit algorithm for dynamic end-to-end routing in SDN-based networks with piecewise-stationary rewards", journal = "Algorithms", year = "2023", volume = "16", number = "5", doi = "10.3390/a16050233", url = "https://www.mdpi.com/1999-4893/16/5/233" }
TY - JOUR TI - A Bayesian multi-armed bandit algorithm for dynamic end-to-end routing in SDN-based networks with piecewise-stationary rewards T2 - Algorithms VL - 16 IS - 5 AU - Santana, P. AU - Moura, J. PY - 2023 SN - 1999-4893 DO - 10.3390/a16050233 UR - https://www.mdpi.com/1999-4893/16/5/233 AB - 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. ER -