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

Mohammad Asif Habibi, Xavier Costa-Pérez, Hans D. Schotten

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.

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

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.
Paper Structure (41 sections, 49 equations, 24 figures, 4 tables, 4 algorithms)

This paper contains 41 sections, 49 equations, 24 figures, 4 tables, 4 algorithms.

Table of Contents

  1. Introductory Remarks
  2. Prior Works
  3. Major Research Challenges
  4. Design Goals and System Objectives
  5. Summary of Key Contributions
  6. The Structure of the Paper
  7. System Model for SliceMapper
  8. Modeling the Network Function Layer
  9. Modeling the Infrastructure Layer
  10. Modeling the Management and Orchestration Layer
  11. Problem Formulation of SliceMapper
  12. Key Objective Function of the Optimization Problem
  13. Linear Programming Formulation of the Problem
  14. Proposed Solution for SliceMapper
  15. Overview of $Q$-Learning-based Solution for VNF Mapping
  16. ...and 26 more sections

Figures (24)

  • Figure 1: Proposed system model for mapping the VNFC of the O-CU and O-DU of an O-RAN slice onto the underlying O-Cloud sites. Note that only the VNFC of an O-gNB and their corresponding virtual resources within the O-Cloud sites, illustrated in the red-dashed box, are within the scope of this article.
  • Figure 2: Episode versus total reward for the on-policy tabular $Q$-learning agent
  • Figure 3: Episode versus episode length for the on-policy tabular $Q$-learning agent
  • Figure 4: Episode versus exploratory actions for the on-policy tabular $Q$-learning agent
  • Figure 5: Episode versus cumulative reward for the on-policy tabular $Q$-learning agent
  • ...and 19 more figures