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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
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)
Exportar Referência (IEEE) 
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
Exportar BibTeX 
@article{santana2023_1734888770815,
	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"
}
Exportar RIS 
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  -