Please use this identifier to cite or link to this item:
http://hdl.handle.net/10071/28693
Author(s): | Santana, P. Moura, J. |
Date: | 2023 |
Title: | A Bayesian multi-armed bandit algorithm for dynamic end-to-end routing in SDN-based networks with piecewise-stationary rewards |
Journal title: | Algorithms |
Volume: | 16 |
Number: | 5 |
Reference: | 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), 233. http://dx.doi.org/10.3390/a16050233 |
ISSN: | 1999-4893 |
DOI (Digital Object Identifier): | 10.3390/a16050233 |
Keywords: | Networks Routing Congestion Variable link delay SDN Algorithm design Multi-armed bandits |
Abstract: | 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. |
Peerreviewed: | yes |
Access type: | Open Access |
Appears in Collections: | ISTAR-RI - Artigos em revistas científicas internacionais com arbitragem científica IT-RI - Artigos em revistas científicas internacionais com arbitragem científica |
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article_95797.pdf | 582,65 kB | Adobe PDF | View/Open |
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