Memory Device Anomaly Detection via Supervisory Model
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Solution Overview
Problem
Existing methods for determining memory device performance are inadequate for real-time monitoring and identifying potential malfunctions, particularly in predicting anomalies that could lead to data corruption or loss, as they fail to accurately assess the physical state of memory devices.
Innovation Solution
A method and system that utilize a supervisory entity computer to monitor I/O operations, apply a pre-determined model to estimate benchmark processing times, and generate performance parameters to detect potential anomalies in memory devices, such as SSDs, by analyzing actual activity times and benchmark processing times, allowing for early flagging of issues to system administrators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If prior art methods (SMART technology) are used to detect drive reliability indicators, then hardware failures can be anticipated, but real-time monitoring of actual performance degradation is insufficient
Solution Approach 1:
The patent replaces mechanical monitoring methods (SMART technology that checks physical indicators) with a computational model-based approach. A supervisory entity computer uses a disk-drive model to simulate and compare expected versus actual performance, enabling precise detection of performance degradation through virtual modeling rather than physical measurement.
Solution Approach 2:
The patent introduces a supervisory entity computer as an intermediary between the memory device and the monitoring system. This intermediary collects performance data, applies predictive models, and generates alerts, serving as a mediator that bridges the gap between raw hardware operation and meaningful reliability assessment.
2Reliability
If traditional monitoring methods are used, then general hardware failures can be detected, but specific real-time performance anomalies leading to data corruption cannot be identified
Solution Approach 1:
The patent performs preliminary actions by establishing a disk-drive model that simulates expected performance characteristics before actual failures occur. The supervisory entity continuously compares real-time performance against this pre-established model, enabling early detection of anomalies that could lead to data corruption before they manifest as complete failures.
Solution Approach 2:
The patent implements a feedback mechanism where the supervisory entity computer continuously monitors performance data, compares it against the disk-drive model predictions, and generates alerts when deviations exceed thresholds. This closed-loop feedback system enables real-time detection and response to performance anomalies that threaten data integrity.
3Reliability
If performance monitoring is implemented, then potential malfunctions can be detected, but the system complexity increases due to additional monitoring infrastructure
Solution Approach 1:
The supervisory entity computer serves multiple functions: it acts as a performance monitor, a predictive model engine, an alert generator, and a historical data analyzer. By consolidating these functions into a single multi-functional system rather than separate specialized components, the patent reduces overall system complexity while maintaining comprehensive monitoring capabilities.
Data Source
AI summary
A method of determining a potential anomaly of a memory device is executable at a supervisory entity computer communicatively coupled to the memory device. The method includes, over a pre-determined period of time, determining a subset of input/output (I/O) operations having been sent to the memory device for processing, applying at least one counter to determine an actual activity time of the memory device during the pre-determined period of time, applying a pre-determined model to generate an estimate of a benchmark processing time for each one of the subset of transactions, calculating a benchmark processing time for the subset of I/O operations, generating a performance parameter based on the actual activity time and the benchmark processing time, and based on an analysis of the performance parameter, determining if the potential anomaly is present in the memory device.