Selecting k Paths with the Minimum Longest Path Length in the Stochastic Semi-Bandit Setting

Abstract:When performing parallel data transmission through a network using multiple paths, it is practically important to minimize the maximum transmission time among the selected paths. This study addresses an online problem in which $k$ paths from an origin vertex to a destination vertex must be selected at each time step within a network represented as a directed graph. Here, the number of paths going through each edge in each parallel data transmission is limited to its capacity, and the time required for transmission is determined stochastically. We formulate the semi-bandit problem of selecting a set of paths to minimize the maximum traversal time among the selected paths and propose an algorithm to solve it.

Machine Learning (cs.LG)

arXiv:2609.14557 [cs.LG]

(or arXiv:2609.14557v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.14557 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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