Automated Anomaly Detection Model Quality Assurance for 5G Networks
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Solution Overview
Problem
The increasing complexity of 5G network operations due to virtualized and containerized network functions (VNFs/CNFs) leads to higher operational challenges in detecting and addressing network failures before they result in service outages, necessitating proactive and automated solutions for anomaly detection and model maintenance.
Innovation Solution
Implementing a system that uses machine learning models to detect anomalies in real-time by analyzing large datasets, with a three-phase approach: discovery, operationalization, and production run-time phases, and automating model quality assurance and deployment to ensure early warning signals and proactive remediation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used for model quality assurance and deployment, then operational control and validation are maintained, but labor intensity increases and productivity decreases
Solution Approach 1:
The system implements automated self-validation through multiple checking mechanisms including syntax validation, semantic validation, and conflict detection that operate without manual intervention. The model quality assurance system automatically evaluates candidate models against predefined criteria and deploys validated models without requiring manual quality assurance operations.
Solution Approach 2:
The system performs preliminary quality checks and validations before model deployment by evaluating candidate models against syntax rules, semantic constraints, and conflict criteria in advance. This preliminary action ensures that only pre-validated models are deployed, preventing quality issues from reaching production environments.
2Reliability
If automated model validation and deployment systems are implemented, then productivity and early detection capability improve, but system complexity increases
Solution Approach 1:
The quality assurance system is divided into distinct modular components including syntax validation modules, semantic validation modules, conflict detection modules, and model deployment modules. Each module handles specific validation tasks independently, making the overall complex system manageable through clear separation of concerns and independent validation layers.
Solution Approach 2:
The system introduces intermediary validation layers between model training and deployment, including a model registry that stores candidate models and their validation results, and a quality assurance engine that mediates between model candidates and deployment targets. These intermediaries manage complexity by providing structured validation pipelines.
3Measurement precision
If multiple validation checks and quality metrics are implemented, then measurement precision and model quality improve, but processing time and computational resources increase
Solution Approach 1:
The validation system applies different quality checks and measurement precision levels to different aspects of model quality. Syntax validation uses strict binary checks, semantic validation uses probabilistic assessments, and conflict detection uses threshold-based evaluations. This local differentiation of validation strictness optimizes processing time while maintaining necessary quality assurance.
Solution Approach 2:
The system implements a multi-tier validation approach where critical syntax errors are checked with excessive precision, while semantic quality uses probabilistic assessments that are less computationally intensive. Not all validation checks are applied with equal depth, allowing the system to balance measurement precision with processing efficiency.
Data Source
AI summary
Systems and methods are provided for automated anomaly detection model quality assurance (QA) and deployment for wireless network failure prediction. Network failure prediction can leverage models trained to detect issues on a network and predict failure scenarios by identifying anomalous issues indicative of failure conditions. To keep these models up to date with changes in network behavior and configurations, the models are recalibrated from time to time. Implementations disclosed herein provide for automated evaluation and deployment of recalibrated models, while assuring issue detection results from the recalibrated models accurately reflect current network conditions. To do this, implementations disclosed herein determine QA metrics for recalibrated, candidate models, QA thresholds from previously deployed models, and QA criteria from a currently deployed model. Based on a comparison of the QA metrics with the QA thresholds and the QA criteria, implementations disclosed herein automatically deploy recalibrated, candidate models without human or external intervention.