Augmented Reality Storage Device Monitoring
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Solution Overview
Problem
Current storage array systems face challenges in accurately monitoring and predicting the end-of-life (EOL) of SSDs and HDDs, leading to potential data loss and costly downtime due to human intervention errors, as existing methods lack consideration for RAID types, IO trends, and error detection.
Innovation Solution
The implementation of augmented reality (AR) technologies that overlay wear-related information, including EOL estimations, onto a real-world view of storage devices using IoT endpoints, machine learning, and analytics, providing a more robust and real-time monitoring system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human observation and manual monitoring of storage device wear is used, then system health awareness can be achieved, but human errors lead to costly downtime and data loss
Solution Approach 1:
The system automatically monitors storage device wear, generates EOL predictions, and provides alerts without requiring human observation. The storage array controller continuously collects wear metrics from SSDs and HDDs, processes this data through analytics engines, and autonomously generates maintenance recommendations, eliminating human error while maintaining reliable monitoring.
Solution Approach 2:
The system implements continuous feedback loops where wear metrics are constantly collected from storage devices, processed through machine learning models, and used to update EOL predictions. These predictions are fed back to administrators through automated alerts and dashboards, enabling proactive maintenance before failures occur, thereby improving reliability without manual intervention.
2Measurement precision
If automated EOL prediction systems are implemented, then monitoring accuracy improves, but system complexity increases
Solution Approach 1:
The monitoring system is segmented into distinct functional modules: wear metric collection from storage devices, data processing through analytics engines, EOL prediction generation, and alert notification. This modular architecture improves measurement precision by dedicating specialized components to each task while managing complexity through clear separation of concerns and standardized interfaces between modules.
Data Source
AI summary
Wear-related information is obtained from one or more storage devices in a storage array system being monitored. One or more graphics representing at least a portion of the wear-related information are generated. The one or more graphics are overlaid onto a real-world view of the one or more storage devices of the storage array system being monitored to generate an augmented reality view illustrating the wear-related information for the one or more storage devices of the storage array system being monitored. The augmented reality view may be presented on a user device. In one example, the wear-related information comprises estimated EOL computations for each of the one or more storage devices.