Isolating Faulty Modules in Clustered Storage via Error Frequency Distribution
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Solution Overview
Problem
In clustered storage systems, identifying and isolating faulty components is challenging due to random redistribution of errors in user data, which can lead to data corruption and system instability, especially since errors are often detected during read operations rather than during initial data writing.
Innovation Solution
A method is implemented to configure a storage system with multiple modules to identify data errors, compute a frequency distribution of these errors across modules, and isolate the faulty module by removing it from the system, ensuring that the frequency distribution is not affected by data redistribution, and can detect system-wide faults to deactivate the entire system if necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is distributed across multiple modules in a clustered storage system, then storage capacity and performance are improved, but the difficulty of detecting and isolating faulty components increases due to random redistribution of errors
Solution Approach 1:
The patent divides the storage system into multiple independent modules, each capable of storing and processing data objects. By segmenting the system, the patent enables localized fault detection and isolation - when errors are detected in data objects, the system can identify which specific module contains the faulty data and isolate only that module, rather than taking down the entire storage system. This segmentation approach maintains high storage capacity while reducing the impact of faults.
Solution Approach 2:
The patent implements a feedback mechanism where modules continuously monitor data integrity through error detection during read operations. When errors are detected, the system feeds back this information to identify the problematic module and isolate it. This feedback loop enables dynamic fault isolation without affecting the entire storage system, allowing the system to maintain capacity while addressing faults efficiently.
2Reliability
If error detection is performed during read operations, then data integrity is maintained, but system stability deteriorates due to random redistribution of errors leading to data corruption
Solution Approach 1:
The patent performs preliminary error detection during read operations before data corruption can propagate. By detecting errors when data is read and immediately isolating the affected module, the system prevents further data corruption and maintains stability. This preliminary action approach ensures that errors are addressed before they can cause widespread system instability.
Solution Approach 2:
The patent extracts the faulty module from the storage system once errors are detected. By removing the problematic module, the system eliminates the source of data corruption and prevents errors from redistributing to other modules. This extraction approach maintains data integrity while restoring system stability by separating the faulty component from the healthy ones.
3Productivity
If the storage system continues operating with faulty modules, then productivity is maintained, but harmful factors increase due to data corruption and system instability
Solution Approach 1:
The patent segments the storage system into independent modules that can be isolated individually. This allows the system to continue operating with healthy modules while separating and removing faulty ones. The segmented architecture ensures that productivity is maintained by keeping good modules online, while harmful factors like data corruption are eliminated by isolating problematic modules.
Solution Approach 2:
The patent extracts and removes faulty modules from the active storage system, preventing them from generating harmful factors like data corruption. By taking out the problematic components while retaining healthy modules, the system maintains productivity without allowing harmful effects to propagate throughout the entire storage system.
Data Source
AI summary
Methods, computing systems and computer program products implement embodiments of the present invention that include configuring, at a first time, a clustered storage system comprising multiple modules to store and process storage requests for respective sets of data objects. While processing the storage requests, respective subsets of the data objects having one or more data errors are identified, and at a second time subsequent to the first time, respective modules storing each of the data objects having at least one data error are identified. computing, based on the identified modules, a frequency distribution of the identified data errors in the data objects over the multiple modules. Based on the frequency distribution, a failure in a given module is identified, and the identified module is removed from the storage system. In some embodiments, prior to the second time, the data objects can be redistributed among the modules of the storage system.