17867086. FAILURE PREDICTION METHOD AND DEVICE FOR A STORAGE DEVICE simplified abstract (SAMSUNG ELECTRONICS CO., LTD.)
Contents
FAILURE PREDICTION METHOD AND DEVICE FOR A STORAGE DEVICE
Organization Name
Inventor(s)
YONGWONG Kwon of Gwangmyeong-si (KR)
FAILURE PREDICTION METHOD AND DEVICE FOR A STORAGE DEVICE - A simplified explanation of the abstract
This abstract first appeared for US patent application 17867086 titled 'FAILURE PREDICTION METHOD AND DEVICE FOR A STORAGE DEVICE
Simplified Explanation
The patent application describes a method and device for predicting failures in storage devices. Here is a simplified explanation of the abstract:
- The method involves inputting real-time SMART data (Self-Monitoring, Analysis, and Reporting Technology) from a storage device into multiple base classification models.
- These base classification models have been trained using historical SMART data from various storage devices or SMART data obtained online.
- The classification models provide a classification result for the real-time SMART data, indicating whether it is healthy or erroneous.
- Based on the classification results from the multiple models, the method determines whether the real-time SMART data is healthy or erroneous.
- By analyzing the number of healthy and erroneous SMART data within a predetermined time window, the method predicts whether the storage device will fail.
Potential applications of this technology:
- Data centers and server farms can use this method to proactively identify storage devices that are at risk of failure, allowing for timely replacements and minimizing downtime.
- Manufacturers of storage devices can integrate this method into their products to provide predictive failure analysis, enhancing the reliability and performance of their devices.
- Cloud service providers can utilize this technology to optimize their storage infrastructure and ensure uninterrupted service to their customers.
Problems solved by this technology:
- Traditional methods of predicting storage device failures rely on manual analysis or simple threshold-based alerts, which may not be accurate or timely. This method provides a more sophisticated and automated approach to failure prediction.
- By utilizing multiple base classification models trained on historical and real-time data, the method improves the accuracy of failure predictions and reduces false positives or negatives.
- The use of real-time SMART data allows for proactive monitoring and early detection of potential failures, preventing data loss and minimizing the impact on business operations.
Benefits of this technology:
- Early detection and prediction of storage device failures can significantly reduce downtime and data loss, leading to improved reliability and availability of critical systems.
- Proactive maintenance and replacement of failing storage devices can save costs by avoiding emergency repairs or complete system failures.
- By optimizing storage device management and resource allocation based on failure predictions, organizations can improve overall efficiency and performance of their storage infrastructure.
Original Abstract Submitted
A failure prediction method and device for a storage device are provided. The method comprises: inputting SMART data of the storage device obtained in real time into each of a plurality of base classification models to obtain a classification result for the SMART data of the storage device obtained in real time that is output by the each classification model, wherein the each base classification model is obtained by training using historical SMART data of a plurality of storage devices and/or SMART data of the plurality of storage devices obtained online; determining whether the SMART data of the storage device obtained in real time is healthy data or erroneous data, based on classification results of the plurality of base classification models; predicting whether the storage device will fail, based on a number of SMART data that is determined as healthy data and a number of SMART data that is determined as erroneous data among SMART data of the storage device obtained within a predetermined time window.
