BMC Deep Learning Error Log Analysis
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Solution Overview
Problem
Information Technology companies face challenges in maintaining high availability of computing devices, as complex failures often require manual analysis, leading to increased downtime and costs due to the need for shipping systems to labs for error diagnosis.
Innovation Solution
A deep learning architecture, utilizing Recurrent Neural Networks within a baseboard management controller, autonomously processes error logs to identify faulty Field Replaceable Units (FRUs) and deconfigures them, enabling real-time fault diagnosis and reduction of downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of error logs is used to diagnose failures, then diagnostic accuracy can be maintained, but system downtime increases and diagnostic speed decreases
Solution Approach 1:
The system performs self-diagnosis by automatically analyzing error logs and identifying faulty FRUs without requiring external manual intervention. The BMC executes the deep learning model locally to autonomously determine the root cause of failures, eliminating the need for shipping systems to labs for analysis.
Solution Approach 2:
The patent replaces manual mechanical analysis with an automated deep learning-based diagnostic system. The Recurrent Neural Network processes error logs and system events algorithmically, substituting human expert analysis with an automated computational model that operates continuously without physical intervention.
2Measurement precision
If complex error analysis requires shipping systems to labs, then diagnostic thoroughness is improved, but productivity and response time deteriorate
Solution Approach 1:
The patent moves the diagnostic capability from a centralized lab environment to the distributed edge device (BMC). By deploying the deep learning model at the network edge, the system enables local real-time analysis without requiring physical transport of hardware, effectively adding a spatial dimension to the diagnostic workflow.
Solution Approach 2:
The BMC acts as an intermediary between the computing device and the deep learning model. It collects error logs and system events, processes them through the neural network, and identifies faulty components, serving as a local intelligence layer that eliminates the need for external lab analysis.
3Measurement precision
If deep learning models are trained on device-specific data, then diagnostic accuracy for that device is improved, but adaptability to other devices decreases
Solution Approach 1:
The patent implements a universal deep learning model that can diagnose multiple types of failures across different computing devices. The Recurrent Neural Network is designed to process various error log formats and system events from different FRUs, making the diagnostic system adaptable to diverse hardware configurations while maintaining accuracy.
Solution Approach 2:
The system adapts to different devices by adjusting model parameters and training data characteristics rather than redesigning the entire diagnostic approach. The deep learning model can be retrained or fine-tuned with device-specific parameters while maintaining the same architectural framework, enabling both specialization and generalization.
Data Source
AI summary
Examples disclosed herein relate to a baseboard management controller (BMC) capable of execution while a computing device is powered to an auxiliary state. The BMC is to process an error log according to a deep learning model to determine one of multiple field replaceable units to deconfigure in response to the error condition. The BMC is to deconfigure the field replaceable unit. The computing device is rebooted. In response to the reboot of the computing device the BMC is to determine whether the error condition persists.