Master of Science (MS)
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.
Liu, Xiang, "Resource Coordination Learning for End-to-end Network Slicing Under Limited State Visibility" (2023). Master's Projects. 1232.
Available for download on Friday, May 24, 2024