Hyperconverged Rack Troubleshooting via Image Recognition
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Solution Overview
Problem
In hyperconverged systems, system administrators face challenges in locating and maintaining specific hardware components across disparate locations within a data center, as these systems are expandable and components are not deterministically configured, making maintenance cumbersome and prone to disruption of other components.
Innovation Solution
A hyperconverged system management approach that uses enhanced image recognition to detect and verify hardware components, combines with GPS to save and locate rack positions, and monitors component health, predicting failures and recommending solutions to prevent outages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If hyperconverged systems use expandable architecture with components physically located at disparate locations, then system scalability is improved, but system administrator maintenance difficulty increases
Solution Approach 1:
The patent creates a digital twin or virtual representation of the physical rack layout and component locations. The image capture system generates a visual map that is stored and processed by the system, allowing administrators to interact with a digital copy of the physical infrastructure rather than physically navigating to locate components. This copying approach resolves the contradiction by maintaining the physical分散 architecture for scalability while providing a centralized digital interface for easy maintenance.
Solution Approach 2:
The patent introduces an image capture device and processing system as an intermediary between the physical hyperconverged system components and the system administrator. The intermediary captures images of rack labels and component locations, processes this visual information, and presents it through a user interface, thereby mediating the interaction between administrators and the dispersed physical components.
2Measurement precision
If system administrators manually locate specific hardware components in dispersed racks, then component identification accuracy can be maintained, but time consumption increases
Solution Approach 1:
The patent replaces the manual mechanical process of physically locating components with an automated image recognition system. Instead of administrators manually searching through racks, the system uses cameras to capture images and automated image processing algorithms to identify component locations, labels, and statuses. This substitution maintains identification accuracy while dramatically reducing the time required.
Solution Approach 2:
The system performs preliminary actions by pre-capturing and storing images of rack layouts, component locations, and label information before maintenance activities are needed. The image capture system proactively documents the physical infrastructure state, so when administrators need to locate components, the information is already processed and ready for immediate retrieval through the user interface.
3Adaptability or versatility
If hyperconverged systems virtualize hardware-defined elements, then system flexibility is improved, but hardware location tracking complexity increases
Solution Approach 1:
The patent merges the physical hardware location tracking with the virtualized system management interface. By capturing images that include both physical rack locations and component identifiers, and integrating this visual information with the virtualization management platform, the system combines physical and virtual asset management into a unified interface. This merging approach maintains system flexibility while simplifying location tracking complexity.
Data Source
AI summary
An approach is provided in which the approach captures an image of a component rack that includes a set of hardware components that are part of a hyperconverged system. The approach discovers the set of hardware components on a computer network during a domain-specific discovery process, and verifies that each one of the set of hardware components captured in the image matches one of the discovered set of hardware components. The approach monitors a status of the set of hardware components in response to verifying that each one of the set of hardware components captured in the image matches one of the discovered set of hardware components.