Server Memory Health Evaluation Model
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Solution Overview
Problem
Existing methods for evaluating server memory health are inadequate as they only determine memory functionality after it has failed, leading to severe impacts like breakdowns, service impairment, and data loss, without prompting for replacement when the memory is not faulty but has a low health degree.
Innovation Solution
A memory evaluation method and apparatus that determine a health degree evaluation model based on influencing factors such as temperature, service load, and error frequencies, providing indication for memory replacement before it fails, using a model that weights these factors to accurately assess memory health.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If memory evaluation is based on fault detection (availability, identification, uncorrectable errors), then the evaluation is simple and direct, but the memory can only be detected after it has failed causing severe impact
Solution Approach 1:
The patent applies preliminary action by evaluating memory health degree before actual failure occurs. The system collects running parameters (temperature, service load, error frequencies) and failure rate parameters, processes them through the health degree evaluation model to predict potential failures in advance, and prompts users to replace memory proactively rather than waiting for fault detection.
Solution Approach 2:
The patent replaces the traditional mechanical fault detection approach with a computational evaluation model. Instead of relying on physical fault indicators (availability, identification, uncorrectable errors), the system uses a health degree evaluation model that processes multiple parameters (running parameters and failure rate parameters) to compute a predictive health metric, substituting direct mechanical detection with algorithmic assessment.
2Reliability
If memory replacement is delayed until fault detection, then replacement cost is minimized, but severe impacts such as breakdown, service impairment, and data loss occur
Solution Approach 1:
The patent implements feedback by continuously monitoring running parameters and failure rate parameters of the memory, processing them through the health degree evaluation model, and providing feedback in the form of health degree indication information. This feedback loop enables proactive identification of deteriorating memory conditions and timely replacement before failure occurs, ensuring server operation continuity.
Solution Approach 2:
The system performs preliminary action by evaluating memory health degree in advance and prompting users to replace memory before actual failure occurs. This proactive approach prevents service impairment and data loss by addressing memory issues during the degradation phase rather than after failure.
3Measurement precision
If multiple health degree influencing factors are evaluated, then the health degree assessment is more accurate, but the evaluation process becomes more complex
Solution Approach 1:
The patent applies segmentation by dividing the health degree evaluation into distinct components: running parameters (temperature, service load, error frequencies) and failure rate parameters. Each parameter is processed separately through the evaluation model, which combines them to compute the overall health degree. This segmentation allows comprehensive assessment while maintaining manageable model structure.
Solution Approach 2:
The patent uses parameter changes by transforming multiple physical and operational parameters (temperature, service load, error frequencies) into a unified health degree metric. The evaluation model processes these diverse parameters and converts them into a single comprehensive indicator that reflects overall memory health, enabling accurate assessment despite the complexity of multiple input factors.
Data Source
AI summary
A memory evaluation method includes determining a health degree evaluation model indicating a relationship in which a health degree of a memory changes with at least one health degree influencing factor of the memory; obtaining at least one running parameter value corresponding to each of the at least one health degree influencing factor; separately matching the at least one running parameter value corresponding to each health degree influencing factor to the health degree evaluation model, to obtain the health degree of the memory; and outputting health degree indication information indicating whether the memory needs to be replaced.