ML Model Management in O-RAN RIC
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Solution Overview
Problem
There is a challenge in effectively monitoring and managing machine learning (ML) models within Open RAN (O-RAN) architectures, particularly between non-real-time (Non-RT) and near-real-time (Near-RT) radio access network intelligent controllers (RICs).
Innovation Solution
The implementation of an ML model management service (A1-ML) over the A1 interface between Non-RT RICs and Near-RT RICs, which enables monitoring services for ML models, collects performance feedback, and addresses anomalies by updating or replacing models with previously known performing models stored at the Non-RT RIC.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If ML models are deployed in Near-RT RIC for real-time network optimization, then network efficiency and performance are improved, but model performance degradation and anomalies may occur over time
Solution Approach 1:
The system implements continuous monitoring of ML model performance metrics (accuracy, loss, resource utilization) and uses this feedback to detect performance degradation. When degradation is detected, the system automatically triggers model updates or replacements, creating a closed-loop feedback mechanism that maintains model reliability while enabling continuous operation in Near-RT RIC.
Solution Approach 2:
The system performs preliminary actions by pre-training and storing multiple ML models at the Non-RT RIC before they are needed. When performance degradation is detected in Near-RT RIC, pre-trained replacement models are already available for immediate deployment, eliminating the need for time-consuming retraining operations during real-time network operation.
2Reliability
If continuous monitoring and management of ML models is implemented, then model reliability is improved, but system complexity increases
Solution Approach 1:
The system segments the ML model management functionality into two distinct components: Non-RT RIC handles model training, storage, and update management, while Near-RT RIC handles real-time inference and performance monitoring. This segmentation allows each component to focus on specific tasks, reducing overall system complexity while maintaining continuous monitoring capabilities.
Solution Approach 2:
The Non-RT RIC acts as an intermediary between model training sources and the Near-RT RIC deployment environment. It manages the model lifecycle including training, validation, storage, and deployment to Near-RT RIC, thereby simplifying the Near-RT RIC's role to primarily inference and monitoring, reducing the complexity burden on the real-time system.
3Reliability
If ML model updates and replacements are performed frequently, then performance degradation is prevented, but network latency and operational overhead increase
Solution Approach 1:
The Non-RT RIC pre-trains and validates multiple replacement models before they are needed, storing them ready for immediate deployment. When performance degradation is detected in Near-RT RIC, pre-validating replacement models are already available, allowing for rapid model updates without time-consuming retraining or validation processes during real-time operation.
Solution Approach 2:
The system implements periodic monitoring of model performance metrics at configured intervals rather than continuous monitoring. This periodic approach reduces the frequency of detection-triggered updates while still maintaining model reliability, thereby reducing unnecessary update operations and associated latency overhead.
Data Source
AI summary
Apparatuses for non real-time (Non-RT) radio access network intelligence controller (RIC) and Near-RT RIC services for machine learning (ML) model management in an open radio access network (O-RAN) are disclosed. The services include ML model monitoring, getting and putting ML models from and to an AI-ML producer and an AI-ML consumer, and terminating the use of an ML mode. The ML model monitoring includes the AI-ML consumer sending monitoring data to the AI-ML producer and the AI-ML producer processing the monitoring data and taking actions based on the monitoring data. The services may be performed over the AI interface using HTTP.