A Bayesian Multi-Armed Bandit Algorithm for Dynamic End-to-End Routing in SDN-Based Networks with Piecewise-Stationary Rewards

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.

Automated summary

To handle the growth of data-intensive network edge services, machine learning is being incorporated into software-defined networking solutions. This article presents a Bayesian multi-armed bandit approach for online dynamic routing of data flows in programmable networking systems. The approach has been analyzed with simulated and emulated data, showing its ability to discover routing paths with minimal delay among alternatives, even when facing abrupt changes in transmission delay distributions.