SliceMapper: Intelligent Mapping of O-CU and O-DU onto O-Cloud Sites in 6G O-RAN

TL;DR

This paper introduces SliceMapper, an intelligent system using reinforcement learning to optimize the placement of O-RAN network components onto cloud sites in 6G networks, improving efficiency and stability.

Contribution

The paper presents a novel reinforcement learning-based framework for mapping O-RAN components onto cloud sites, with comprehensive evaluation of multiple algorithms and representations.

Findings

On-policy function approximation outperforms other variants in stability.

Tabular methods achieve higher average rewards.

Reinforcement learning effectively optimizes O-RAN component placement.

Abstract

In this paper, we propose an rApp, named SliceMapper, to optimize the mapping process of the open centralized unit (O-CU) and open distributed unit (O-DU) of an open radio access network (O-RAN) slice subnet onto the underlying open cloud (O-Cloud) sites in sixth-generation (6G) O-RAN. To accomplish this, we first design a system model for SliceMapper and introduce its mathematical framework. Next, we formulate the mapping process addressed by SliceMapper as a sequential decision-making optimization problem. To solve this problem, we implement both on-policy and off-policy variants of the Q-learning algorithm, employing tabular representation as well as function approximation methods for each variant. To evaluate the effectiveness of these approaches, we conduct a series of simulations under various scenarios. We proceed further by performing a comparative analysis of all four variants. The results demonstrate that the on-policy function approximation method outperforms the alternative approaches in terms of stability and lower standard deviation across all random seeds. However, the on-policy and off-policy tabular representation methods achieve higher average rewards, with values of 5.42 and 5.12, respectively. Finally, we conclude the paper and introduce several directions for future research.