Graph-Based Error Source Identification in Storage Systems
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Solution Overview
Problem
Current enterprise-level storage systems with complex topologies face challenges in accurately identifying problematic components, leading to inefficient maintenance, high costs, and user dissatisfaction due to reliance on subjective expert methods and cross-team complexities.
Innovation Solution
A method and device that abstract the storage system's topology as a graph, processing error information from history logs to identify problematic components by determining error sources through directed acyclic graphs and the Lowest Common Ancestor algorithm, reducing manual effort and increasing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the storage system uses a complex topology structure with many components to meet higher data storage capacity and read/write speeds, then the data storage capacity and performance are improved, but the system becomes more prone to errors and more difficult to maintain
Solution Approach 1:
The patent segments the complex storage system into individual components (relay devices, storage devices, cables) and represents them as separate nodes in a graph structure. This segmentation allows the system to track and analyze errors at the component level rather than treating the entire system as a black box, thereby maintaining reliability in complex topologies.
Solution Approach 2:
The patent introduces an intermediary error analysis system that collects error logs from various components and uses graph theory to analyze error propagation patterns. This intermediary layer between the physical components and maintenance staff enables accurate identification of error sources without requiring direct intervention in the complex hardware topology.
2Adaptability or versatility
If the storage system includes many relay devices and connection components to support complex topologies, then the system versatility and storage capacity are improved, but the difficulty of locating and identifying errors increases
Solution Approach 1:
The patent replaces manual error detection methods with an automated electronic system based on graph theory algorithms. Instead of physically tracing connections through complex topologies, the system uses computational algorithms to analyze error logs and identify error sources, dramatically reducing the difficulty of error detection in versatile configurations.
Solution Approach 2:
The system implements self-service error identification by automatically collecting error logs from all components, analyzing propagation patterns through the graph structure, and identifying error sources without requiring expert intervention. This enables the system to diagnose its own problems regardless of topology complexity.
3Measurement precision
If manual expert methods are used to identify error sources in complex storage systems, then some level of error detection is possible, but the maintenance time and costs increase significantly
Solution Approach 1:
The patent implements preliminary action by continuously collecting and storing error logs from all components before actual errors occur. When an error does occur, the pre-collected log data and pre-built graph structure enable immediate analysis and rapid identification of error sources, eliminating the need for time-consuming manual investigation.
Solution Approach 2:
The system establishes feedback loops where error logs from all components are continuously monitored and fed into the graph analysis system. This real-time feedback mechanism enables the system to automatically detect and identify error sources as they occur, providing rapid response without manual intervention and significantly reducing maintenance time.
Data Source
AI summary
Embodiments of the present disclosure relate to a method and device for identifying a problematic component in a storage system. The method comprises determining, based on history error logs of components of the storage system, a graph indicating error information of the components, where nodes in the graph indicate the components, and edges in the graph indicate connections between the components; the method further comprises identifying, based on the graph, an error source in the components of the storage system to be the problematic component. With the method and device of the present disclosure, the error source in the storage system can be identified more accurately and effectively and user experience can be enhanced.