Fairness Guaranteed and Auction-based x-haul and Cloud Resource Allocation in Multi-tenant O-RANs

TL;DR

This paper introduces auction-based and fairness-oriented resource allocation mechanisms for multi-tenant O-RANs, optimizing x-haul and cloud resources to improve economic efficiency and fairness among diverse mobile network operators.

Contribution

It proposes and compares min-max fairness and VCG auction-based mechanisms for resource allocation in multi-tenant O-RANs, including novel heuristics for enhanced efficiency.

Findings

VCG auction minimizes total OPEX and ensures truthful demands.

Min-max fairness reduces maximum OPEX through proportional cost-sharing.

Heuristics outperform traditional algorithms in resource utilization and economic efficiency.

Abstract

The open-radio access network (O-RAN) embraces cloudification and network function virtualization for base-band function processing by dis-aggregated radio units (RUs), distributed units (DUs), and centralized units (CUs). These enable the cloud-RAN vision in full, where multiple mobile network operators (MNOs) can install their proprietary or open RUs, but lease on-demand computational resources for DU-CU functions from commonly available open-clouds via open x-haul interfaces. In this paper, we propose and compare the performances of min-max fairness and Vickrey-Clarke-Groves (VCG) auction-based x-haul and DU-CU resource allocation mechanisms to create a multi-tenant O-RAN ecosystem that is sustainable for small, medium, and large MNOs. The min-max fair approach minimizes the maximum OPEX of RUs through cost-sharing proportional to their demands, whereas the VCG auction-based approach minimizes the total OPEX for all resources utilized while extracting truthful demands from RUs. We consider time-wavelength division multiplexed (TWDM) passive optical network (PON)-based x-haul interfaces where PON virtualization technique is used to flexibly provide optical connections among RUs and edge-clouds at macro-cell RU locations as well as open-clouds at the central office locations. Moreover, we design efficient heuristics that yield significantly better economic efficiency and network resource utilization than conventional greedy resource allocation algorithms and reinforcement learning-based algorithms.