Edge Health Report Engine for Telemetry Data Compaction
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Solution Overview
Problem
Conventional computing device health determination and reporting systems face issues in edge environments due to high telemetry data requirements, network congestion, and limited processing resources, leading to inaccurate health reports and increased costs.
Innovation Solution
An Information Handling System (IHS) with a health report generation engine that compacts telemetry data and performs inference operations locally to generate health reports, reducing data transmission and processing burdens, and utilizing custom-written inference module code optimized for edge environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If centralized health report generation is used with full telemetry data transmission, then comprehensive health analysis is achieved, but network bandwidth consumption and data transmission time increase significantly
Solution Approach 1:
The patent extracts and performs critical health analysis functions locally at the edge device, separating the inference operations from the centralized cloud system. This extraction allows comprehensive health monitoring while minimizing network data transmission by only sending essential telemetry data or alerts rather than complete datasets.
Solution Approach 2:
The system segments the health monitoring architecture into edge-based inference operations and centralized model training/update functions. This segmentation enables local real-time health assessment with minimal network dependency while maintaining centralized control for model improvements.
2Measurement precision
If full telemetry data is collected and transmitted for health analysis, then accurate health reports are generated, but processing time and latency increase
Solution Approach 1:
The system performs preliminary health inference operations locally at the edge device using pre-trained models. This preliminary action enables real-time health assessment without waiting for centralized processing, significantly reducing latency while maintaining accuracy through periodic model updates from the centralized system.
3Loss of information
If comprehensive telemetry data is stored and analyzed centrally, then complete health insights are obtained, but storage space requirements increase
Solution Approach 1:
The patent extracts critical health insights through local inference operations, eliminating the need to store and transmit complete telemetry datasets to centralized systems. Only essential data or processed results need to be stored centrally, dramatically reducing storage requirements while preserving health information completeness.
4Productivity
If custom inference modules are developed for edge environments, then processing efficiency and accuracy improve, but development complexity and resource requirements increase
Solution Approach 1:
The system optimizes inference models by changing parameters such as model size, complexity, and computational requirements to suit edge device constraints. This parameter adjustment enables efficient local health analysis while keeping development and deployment manageable through standardized optimization processes.
Data Source
AI summary
A component health determination and reporting system includes a computing device having a computing device component. The computing device component includes computing device component subsystem(s) coupled to a health report generation subsystem. The health report generation subsystem receives telemetry data from each of the computing device component subsystem(s), performs data compaction operations on the telemetry data to generate compacted telemetry data, performs inference operations on the compacted telemetry data to generate a health report for the computing device component, and provides the health report to the computing device.