Storage Controller Recovery via Machine Learning
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Solution Overview
Problem
Storage controllers face challenges in expeditiously determining the best recovery mechanism due to varying configurations and components, often relying on administrator experience which can be inconsistent and not always optimal.
Innovation Solution
A machine learning module, specifically a neural network, is employed to analyze inputs and generate output values for various recovery mechanisms, allowing the selection of the most effective recovery strategy based on weighted and biased calculations, which can be shared among multiple storage controllers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If administrator experience is used to determine recovery mechanism, then operational simplicity is maintained, but recovery effectiveness and consistency deteriorate
Solution Approach 1:
The storage controller automatically determines the optimal recovery mechanism using a machine learning module that analyzes controller attributes and failure conditions. The system self-diagnoses and self-recovers without administrator intervention, eliminating inconsistency while maintaining operational simplicity through automated decision-making based on learned patterns from historical failure data
Solution Approach 2:
The patent replaces the mechanical dependency on administrator knowledge and decision-making with an electronic machine learning system. The neural network processes controller attributes and failure information to automatically select recovery mechanisms, substituting human cognitive processes with computational algorithms that provide consistent, data-driven decisions
2Productivity
If traditional recovery methods are used, then system simplicity is maintained, but recovery time and operational efficiency worsen
Solution Approach 1:
The machine learning module is pre-trained with historical failure data and controller attribute information before actual failures occur. When a failure happens, the system immediately applies pre-learned knowledge to determine the optimal recovery mechanism, eliminating the time required for administrators to analyze conditions and select appropriate recovery actions
Solution Approach 2:
The system implements a feedback mechanism where recovery outcomes are continuously monitored and fed back to the machine learning module. This allows the system to learn from actual recovery results and improve its decision-making over time, progressively reducing recovery time and improving operational efficiency through iterative optimization
3Adaptability or versatility
If standardized recovery procedures are used across different configurations, then ease of operation is improved, but adaptability to specific controller configurations deteriorates
Solution Approach 1:
The machine learning module analyzes specific controller attributes including hardware configuration, firmware version, cache size, and component details to determine the optimal recovery mechanism tailored to each controller's unique characteristics. This localized analysis ensures that recovery actions are specifically adapted to the actual controller configuration rather than applying generic procedures
Solution Approach 2:
The system dynamically adjusts recovery strategy based on varying controller parameters such as cache capacity, hardware components, firmware version, and operational workload. The machine learning module processes these parameter variations to select recovery mechanisms optimized for each specific configuration, achieving both adaptability and operational simplicity through data-driven parameter-based decision-making
Data Source
AI summary
In response to an occurrence of a failure in a storage controller, an input on a plurality of attributes of the storage controller at a time of occurrence of the failure is provided to a machine learning module. In response to receiving the input, the machine learning module generates a plurality of output values corresponding to a plurality of recovery mechanisms to recover from the failure in the storage controller. A recovery is made from the failure in the storage controller, by applying a recovery mechanism whose output value is greatest among the plurality of output values that are generated by the machine learning module.