Publication Date

Spring 2023

Degree Type

Master's Project

Degree Name

Master of Science (MS)


Computer Science

First Advisor

Genya Ishigaki

Second Advisor

Mike Wu

Third Advisor

Navrarti Saxena


Resource Coorindation, CMAB, LLR


This paper discusses a resource coordination problem under limited state visibility to realize end-to-end network slices that are hosted by multiple network domains. We formulate this resource coordination problem as a special type of the multi- armed bandit (MAB) problem called the combinatorial multi-armed bandit (CMAB) problem. Based on this formulation, we convert the problem to a regret minimization problem with a linear objective function and solve it by adapting the Learning with Linear Rewards (LLR) algorithm. In this paper, we present a new hybrid approach that incorporates state reports, which include partial resource information in each domain, into the existing LLR algorithm. Our experiment results show that the proposed algorithm performs significantly better than other baseline learning algorithms. Furthermore, the proposed algorithm outperforms the LLR algorithm especially when the traffic patterns of network slices are more dynamic and unstable.

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