DIMM Failure Predictor Using Age-Adaptive Leaky Bucket Algorithm
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Solution Overview
Problem
Information handling systems face challenges in predicting and managing memory failures in Double-Data Rate Dual In-Line Memory Modules (DIMMs), as correctable read errors increase with DIMM age, potentially leading to uncorrectable errors and system failure, without effective proactive warning mechanisms.
Innovation Solution
An information handling system that includes a processor, a dual in-line memory module (DIMM), and a memory controller, which uses a failure predictor implementing a leaky-bucket algorithm to track correctable errors, providing progressive warnings based on error thresholds and age-related settings to anticipate and prevent uncorrectable errors by dynamically adjusting error leak rates and thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a failure predictor accumulates error counts to predict memory failures, then prediction accuracy improves, but false alarms increase due to transient errors
Solution Approach 1:
The patent applies dynamics by making the error accumulation mechanism adaptive rather than static. The leaky bucket algorithm dynamically adjusts the error count based on the age of the memory device, using different accumulation rates for different age periods. This resolves the contradiction by allowing the system to be sensitive to genuine degradation trends while being less sensitive to transient errors in newer devices, thereby improving prediction accuracy without excessive false alarms.
Solution Approach 2:
The patent changes the parameter of error accumulation rate based on memory device age. By implementing age-dependent thresholds and accumulation rates in the leaky bucket algorithm, the system adapts its sensitivity to errors. Newer devices use higher thresholds to filter out transient errors, while older devices use lower thresholds to detect genuine degradation, thus resolving the contradiction between detection sensitivity and false alarm rate.
2Measurement precision
If error thresholds are set low to detect early failures, then detection sensitivity improves, but normal wear-and-tear errors trigger false warnings
Solution Approach 1:
The patent applies local quality by implementing different error threshold levels for different stages of memory device life. The leaky bucket algorithm uses age-specific parameters where newer devices have higher thresholds and older devices have lower thresholds. This resolves the contradiction by making the detection sensitivity locally appropriate to each device's lifecycle stage, preventing false warnings from normal wear-and-tear in younger devices while maintaining high sensitivity in older devices.
Solution Approach 2:
The patent implements preliminary action by pre-configuring age-dependent thresholds and accumulation rates based on expected memory device lifecycles. The system proactively adjusts its detection parameters before false warnings would occur, using the leaky bucket algorithm to pre-establish appropriate sensitivity levels for different device ages. This prevents the contradiction from manifesting by having the right thresholds in place before the problem arises.
3Measurement precision
If the system monitors all correctable errors continuously, then failure prediction accuracy improves, but system performance degrades due to processing overhead
Solution Approach 1:
The patent extracts only the essential error information needed for prediction rather than processing all error data continuously. The leaky bucket algorithm selectively accumulates and filters error counts, extracting only the relevant degradation trend information while discarding transient noise. This resolves the contradiction by maintaining prediction accuracy through selective monitoring while reducing processing overhead compared to continuous detailed analysis of all errors.
Solution Approach 2:
The patent implements periodic action through the leaky bucket algorithm's structured error accumulation approach. Instead of continuous monitoring and analysis, the system periodically updates error counts using predetermined accumulation rates and thresholds. This resolves the contradiction by achieving adequate prediction accuracy through periodic sampling rather than continuous processing, thereby maintaining system performance while still detecting failure trends.
Data Source
AI summary
An information handling system includes a processor, a dual in-line memory module (DIMM), and a memory controller coupled to the DIMM. The memory controller provides interrupts to the processor each time a read transaction from the DIMM results in a correctable read error. The processor instantiates a failure predictor to receive the interrupts, accumulate a count of the interrupts, and provide a first error indication when the count exceeds a first error threshold. The failure predictor increments the count each time the predictor receives a particular interrupt and decrements the count in accordance with an error leak rate. The error leak rate has a first value when the DIMM is newer than a first age threshold and has a second value when the DIMM is older than the first age threshold.