Profiling Operating Efficiency Deviations in Computing Systems
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Solution Overview
Problem
Data centers with numerous computing systems face challenges in detecting and addressing operating efficiency deviations due to slight modifications in configurations, such as poor cabling, internal wiring, and dust buildup, which can lead to reduced performance and higher temperatures, without providing adequate notifications to system administrators.
Innovation Solution
A method for profiling operating efficiency deviations involves generating a profile of expected efficiency in an ideal configuration, monitoring and identifying deviations in alternative configurations, and recording associations of deviations with their root causes, allowing for notifications to system administrators in both controlled and performance environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If computing systems operate with slight configuration modifications (poor cabling, internal wiring variations, dust buildup), then the system can continue to operate without notification, but operating efficiency decreases and temperatures increase
Solution Approach 1:
The system performs preliminary actions by generating a profile of expected operating efficiency for the computing system in an ideal configuration before actual operation. This baseline profile enables future comparisons to detect deviations caused by configuration modifications, dust buildup, or other environmental factors, allowing proactive identification of efficiency degradation before it becomes critical
Solution Approach 2:
The system implements continuous feedback by monitoring operating efficiency during runtime, comparing actual performance against the pre-generated ideal profile, and identifying deviations. This feedback mechanism triggers notifications to system administrators when deviations exceed thresholds, enabling timely corrective actions to restore optimal efficiency while maintaining system operation
2Measurement precision
If the system monitors for deviations continuously, then early detection of efficiency issues is achieved, but system complexity and monitoring overhead increase
Solution Approach 1:
The system applies partial monitoring by focusing only on key performance indicators and critical deviations from the ideal profile rather than comprehensively monitoring every system parameter. This selective approach maintains high detection accuracy for significant efficiency deviations while avoiding the complexity and overhead of exhaustive monitoring of all possible configuration variations
Solution Approach 2:
The system utilizes parameter changes by establishing threshold values for acceptable deviations from the ideal operating profile. When monitored parameters exceed these thresholds, the system triggers notifications. This parameter-based approach simplifies the monitoring logic while maintaining precise detection of meaningful efficiency deviations, avoiding the need for complex analysis of every minor variation
Data Source
AI summary
Profiling operating efficiency deviations of a computing system includes: generating a profile of expected operating efficiency for a computing system in an ideal configuration; for each of a plurality of alternative configurations of the computing system, wherein each of the alternative configurations includes a variation of the ideal configuration that introduces a deviation in operating efficiency of the computing system, said variation comprising a root cause of the deviation: monitoring operating efficiency of the computing system identifying, from the monitored operating efficiency, a deviation of operating efficiency from the expected operating efficiency; and recording, in a data structure, an association of the deviation and the root cause of the deviation.