BMC Data Volume Control for Hardware Failure Prediction
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Solution Overview
Problem
The increasing workload on Baseboard Management Controllers (BMCs) due to growing management tasks leads to latency in services such as thermal control, sensor monitoring, and firmware updates, affecting overall server performance.
Innovation Solution
The BMC identifies its current utilization level, obtains hardware performance data, runs an application program for hardware failure prediction, and controls the amount of data used by the application program based on the utilization level, thereby optimizing CPU resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the BMC performs more management tasks and hardware failure prediction, then the functionality and capacity of the BMC is improved, but the processor utilization increases causing latency in critical services
Solution Approach 1:
The patent applies partial action by controlling the amount of hardware performance data used by the failure prediction application based on processor utilization levels. When utilization is high, the system reduces the data volume processed by the prediction application, allowing it to run partially rather than fully, thus preventing overload while maintaining the capability when resources are available
Solution Approach 2:
The system dynamically adjusts the amount of hardware performance data processed by the failure prediction application based on real-time processor utilization monitoring. This dynamic adjustment allows the BMC to adapt its functionality according to current workload conditions, maintaining critical services during high utilization while enabling enhanced prediction capabilities during low utilization periods
2Reliability
If the BMC runs hardware failure prediction applications, then proactive hardware failure prediction capability is improved, but the processor becomes overloaded affecting critical management functions
Solution Approach 1:
The patent implements feedback control by continuously monitoring processor utilization levels and using this information to adjust the amount of hardware performance data processed by the failure prediction application. This closed-loop feedback mechanism ensures that the prediction application adapts to current system conditions, maintaining reliability while preventing processor overload that would degrade overall productivity
Solution Approach 2:
The system changes the parameter of data volume processed by the failure prediction application based on processor utilization thresholds. By adjusting this parameter dynamically, the system maintains the prediction capability (reliability) while controlling the computational load to preserve processor performance for critical management functions
Data Source
AI summary
A computer program product, a method and a baseboard management controller for performing the operations of computer program product or method. The operations include identifying a current utilization level of the processor of the baseboard management controller, obtaining hardware performance data for hardware devices installed in a server that includes the baseboard management controller, running an application program that performs hardware failure prediction, and controlling an amount of the hardware performance data that is used by the application program to perform the hardware failure prediction, wherein the amount of the hardware performance data is controlled as a function of the current utilization level of the processor.