Automated Storage Component Repair via Data Analytics
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Solution Overview
Problem
Conventional repair procedures for data storage systems are slow and unreliable due to manual information collection, which hampers efficient diagnosis and correction of component issues.
Innovation Solution
An automated method that collects performance metric data, identifies anomalous behavior, maps it to defects, and performs corrective operations using data analytics and machine learning to quickly and reliably repair components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual information collection and diagnosis procedures are used, then technical support personnel can identify component defects, but the repair process becomes slow and unreliable
Solution Approach 1:
The system performs self-diagnosis by automatically collecting component metric data, identifying anomalous behavior patterns, and mapping them to defects without requiring manual technical support intervention. The storage system autonomously monitors its own components, detects performance degradation, and initiates repair workflows, eliminating the slow manual information collection process while maintaining high diagnostic accuracy
Solution Approach 2:
The patent replaces the mechanical manual process of information collection and diagnosis with an automated data analytics system. Machine learning models and algorithms automatically analyze component metric data, identify anomalous patterns, and map them to specific defects, substituting human technical support activities with computational processes that are both faster and more reliable
2Measurement precision
If manual information collection is used, then component defects can be diagnosed, but the process is slow and unreliable
Solution Approach 1:
The system replaces manual diagnostic procedures with automated data analytics and machine learning models that continuously analyze component metric data. These computational systems provide both high measurement precision through pattern recognition and high productivity through automated, continuous operation without human intervention
Solution Approach 2:
The automated monitoring system operates continuously, constantly collecting and analyzing component metric data without interruption. This continuous operation enables real-time defect detection with high precision while maintaining high diagnostic speed, as the system never stops processing data unlike manual methods
3Productivity
If automated data analytics and machine learning are applied, then repair speed and reliability are significantly improved, but system complexity increases
Solution Approach 1:
The system employs universal machine learning models and data analytics frameworks that can diagnose multiple types of component defects across different storage system components. This multi-functional approach achieves high repair speed and reliability without proportionally increasing complexity, as the same analytical infrastructure handles diverse diagnostic tasks
Solution Approach 2:
The patent introduces an intermediary layer of data analytics and machine learning models that sits between raw component metric data and diagnostic conclusions. This intermediary layer automates the complex analysis process, enabling fast and reliable repairs while containing system complexity within the intermediary layer rather than propagating it throughout the entire system
Data Source
AI summary
An improved technique involves automatically applying a mapping from anomalous values of a parameter that describes the performance of a component to a defect in the component. As part of operations of a data storage system, a computer collects data related to performance metrics of components of the data storage system. Upon receiving the data, the computer determines sequences of values of the performance metrics that indicate anomalous behavior of the component. The computer than maps these values to a defect of the component, and performs a corrective operation on the component to correct the defect.