Storage Device Failure Warning Model Using Decision Tree Extraction
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Solution Overview
Problem
Existing storage device warning systems, such as centralized monitoring and warning systems and SSD self-warning systems, face challenges including high deployment and maintenance costs, undetectable failures due to network and database issues, and limited ability to detect failures in a timely manner.
Innovation Solution
A method for constructing a failure warning model for storage devices using a decision tree-based approach, where an initial failure warning model is trained with a random forest algorithm based on historical failure-related data, and high-frequency decision nodes are identified and used to construct a new decision tree model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a centralized monitoring and warning system is deployed, then the coverage and comprehensiveness of failure detection is improved, but the deployment cost, operation cost, and maintenance cost increase significantly
Solution Approach 1:
The patent extracts the failure detection function from the complex centralized monitoring system and implements it within the storage device itself through a simplified self-warning mechanism. The decision tree model is built and executed locally in the storage device, eliminating the need for centralized monitoring infrastructure while maintaining failure detection capability.
Solution Approach 2:
The storage device performs self-monitoring and self-warning using an embedded decision tree model. The device independently collects operational attributes, evaluates them against the warning model, and generates warnings without requiring external centralized monitoring systems, thereby reducing system complexity while maintaining reliability.
2Reliability
If a centralized monitoring and warning system is used, then the comprehensiveness of failure detection is improved, but data loss, long collection interval, and SN errors occur due to network and database issues
Solution Approach 1:
The patent removes the data collection and transmission环节 from the centralized monitoring architecture by implementing local self-monitoring within the storage device. Operational attributes are collected and evaluated locally without being transmitted through networks or databases, eliminating the risk of data loss and collection interruptions associated with centralized systems.
Solution Approach 2:
The storage device independently monitors its own operational status and maintains local records of operational attributes without relying on external databases or networks. This self-service approach ensures continuous, reliable data collection without the network dependency that causes data loss and collection delays in centralized systems.
3Device complexity
If a single attribute threshold judgment is used in SSD self-warning system, then the simplicity of the system is maintained, but the ability to detect limited type of failure is reduced
Solution Approach 1:
The patent segments the warning model into multiple decision trees, each handling different failure modes or operational scenarios. This segmentation allows the system to maintain simplicity through modular decision structures while expanding failure detection capability by evaluating multiple attributes through different decision paths in the forest.
Solution Approach 2:
The decision tree warning model is designed to handle multiple types of failures and operational attributes through a unified framework. The model can evaluate various operational attributes (temperature, write endurance, read errors, etc.) and adapt to different failure modes, providing universal failure detection capability while maintaining system simplicity.
4Measurement precision
If a strict preset threshold is used in SSD self-warning system, then the precision of failure detection is improved, but the timeliness of failure detection is reduced due to missed detections
Solution Approach 1:
The patent replaces static strict thresholds with a dynamic decision tree model that can adapt to different operational conditions. The model evaluates multiple attributes simultaneously and can trigger warnings based on combinations of conditions, enabling timely detection of failures that would be missed by single-threshold approaches while maintaining precision through structured decision logic.
Data Source
AI summary
A method for operating a storage device and the storage device are provided. The method includes obtaining an initial failure warning model comprising a plurality of decision trees, the obtaining the initial failure warning model including being trained with a random forest algorithm based on historical failure related data, the historical failure related data comprising historical multiple operational attributes data of the storage device and failure logs of the storage device, determining high frequency decision nodes in the plurality of decision trees, the high frequency decision nodes being similar single-nodes with at least a first number in the plurality of decision trees, the similar single-nodes being single-nodes having a same monitoring attribute, a same attribute determination symbol, and a difference between attribute thresholds within a first range, and constructing the failure warning model of a new decision tree comprising the high frequency decision nodes.