Cloud-Based Cellular Core Health Monitoring With Vendor-Specific Queries
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Solution Overview
Problem
The complexity of modern 5G New Radio (NR) cellular networks, with components sourced from different vendors and executed as special-purpose software on general-purpose hardware, complicates monitoring, analysis, and diagnosis, making it difficult to locate and test particular network functions effectively.
Innovation Solution
A method for health monitoring of cellular network functions in a cloud-computing environment, involving a cellular network health monitoring system that performs customized test queries on cloud-based network components, identifies instantiations, and outputs status information, utilizing a library of test query files tailored to specific vendors and types of network functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cellular network components are sourced from different vendors and executed as virtualized software on general-purpose hardware, then network flexibility and adaptability are improved, but monitoring and diagnosis complexity increases
Solution Approach 1:
The monitoring system segments the complex monitoring task into distinct components: a user interface layer for user interactions, a drill engine layer for coordinate translation and test query generation, and a library of vendor-specific test query files. This segmentation allows each component to handle specific aspects of monitoring independently, reducing overall system complexity despite multi-vendor environments.
Solution Approach 2:
The drill engine acts as an intermediary between the user interface and the diverse vendor-specific network functions. It translates generic user requests into vendor-specific test queries by maintaining coordinates that map user interface elements to underlying network function instantiations, pod names, and test query parameters. This intermediary layer shields users from vendor-specific complexities while maintaining adaptability.
2Measurement precision
If customized test queries are performed on specific network function instantiations and pods, then measurement precision is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-defining coordinates that map user interface elements to specific network function instantiations, pod names, and test query parameters. These coordinates are established in advance and stored in the drill engine, allowing the system to quickly translate user requests into precise test queries without real-time complex mappings, thereby achieving measurement precision without proportional complexity increase.
Solution Approach 2:
The coordinate system creates an equipotential layer that equalizes the complexity of accessing different vendor-specific network functions. By standardizing the mapping approach across all vendors through a unified coordinate structure, the system makes precise monitoring of any network function as accessible as any other, despite underlying vendor-specific differences.
3Ease of operation
If a comprehensive library of vendor-specific test query files is maintained, then ease of operation is improved, but loss of information increases due to management complexity
Solution Approach 1:
The drill engine implements a universal coordinate system that can map to multiple vendor-specific test query files simultaneously. This multi-functional coordinate structure allows the same user interface to operate across different vendors and network function types without requiring separate management approaches, reducing information loss through standardized handling of diverse test query files.
Data Source
AI summary
Various arrangements for performing health monitoring of cellular network functions in a cloud-computing environment are presented. A cloud computing region may be input for analysis. A cellular network core may be implemented on a cloud computing platform. Instantiations of the cellular core function and pods of the cellular core function executed within the cloud computing region can be identified. A test query can be performed based on the input of the cellular core function. A status of the cellular core function can be output based on the test query.