Federated Machine Reasoning for Resource Provisioning in 6G O-RAN

TL;DR

This paper introduces FLMR, a neurosymbolic federated reasoning framework for resource provisioning in 6G O-RAN, improving CPU demand prediction and balancing resource allocation with enhanced transparency.

Contribution

The paper presents a novel neurosymbolic federated reasoning approach for resource management in 6G O-RAN, ensuring transparency and outperforming baseline methods.

Findings

Outperforms DeepCog baseline in CPU demand prediction.

Reduces resource overprovisioning and underprovisioning by a factor of 6.

Enhances transparency and human understanding in AI/ML decisions.

Abstract

O-RAN specifications reshape RANs with function disaggregation and open interfaces, driven by RAN Intelligent Controllers. This enables data-driven management through AI/ML but poses trust challenges due to human operators' limited understanding of AI/ML decision-making. Balancing resource provisioning and avoiding overprovisioning and underprovisioning is critical, especially among the multiple virtualized base station(vBS) instances. Thus, we propose a novel Federated Machine Reasoning (FLMR) framework, a neurosymbolic method for federated reasoning, learning, and querying. FLMR optimizes CPU demand prediction based on contextual information and vBS configuration using local monitoring data from virtual base stations (vBS) on a shared O-Cloud platform.This optimization is critical, as insufficient computing resources can result in synchronization loss and significantly reduce network throughput. In the telecom domain, particularly in the virtual Radio Access Network (vRAN) sector, predicting and managing the CPU load of vBSs poses a significant challenge for network operators. Our proposed FLMR framework ensures transparency and human understanding in AI/ML decisions and addresses the evolving demands of the 6G O-RAN landscape, where reliability and performance are paramount. Furthermore, we performed a comparative analysis using \textit{DeepCog} as the baseline method. The outcomes highlight how our proposed approach outperforms the baseline and strikes a better balance between resource overprovisioning and underprovisioning. Our method notably lowers both provisioning relative to the baseline by a factor of 6.