Samsung electronics co., ltd. (20240160511). FAILURE PREDICTION APPARATUS AND METHOD FOR STORAGE DEVICES simplified abstract
Contents
- 1 FAILURE PREDICTION APPARATUS AND METHOD FOR STORAGE DEVICES
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 FAILURE PREDICTION APPARATUS AND METHOD FOR STORAGE DEVICES - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
FAILURE PREDICTION APPARATUS AND METHOD FOR STORAGE DEVICES
Organization Name
Inventor(s)
Yongwong Kwon of SUWON-SI (KR)
FAILURE PREDICTION APPARATUS AND METHOD FOR STORAGE DEVICES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240160511 titled 'FAILURE PREDICTION APPARATUS AND METHOD FOR STORAGE DEVICES
Simplified Explanation
The patent application describes a method for predicting failures in storage devices by obtaining attribute information, generating global attribute information, and using a machine-learning model for prediction.
- Obtaining attribute information of multiple attributes for multiple storage devices.
- Generating global attribute information for each storage device based on past attribute information.
- Predicting failures in storage devices using a machine-learning model based on current and past attribute information.
Potential Applications
This technology can be applied in various industries where the failure of storage devices can lead to data loss or system downtime. Some potential applications include:
- Data centers
- Cloud storage providers
- Industrial automation systems
Problems Solved
This technology helps in proactively identifying potential failures in storage devices, allowing for preventive maintenance and minimizing the risk of data loss or system downtime. Some problems solved by this technology include:
- Unplanned downtime due to storage device failures
- Data loss due to unexpected storage device malfunctions
Benefits
The benefits of this technology include:
- Improved reliability of storage devices
- Reduced maintenance costs
- Increased data security and availability
Potential Commercial Applications
With the increasing reliance on data storage systems, the commercial applications of this technology are vast. Some potential commercial applications include:
- Storage device manufacturers
- Data recovery services
- IT service providers
Possible Prior Art
One possible prior art in this field is the use of SMART (Self-Monitoring, Analysis, and Reporting Technology) data in predicting storage device failures. SMART data has been used to monitor the health of storage devices and predict failures based on predefined thresholds.
What are the limitations of the machine-learning model used for failure prediction in storage devices?
The machine-learning model may not account for all possible failure scenarios, leading to false positives or false negatives in failure predictions. Additionally, the model's accuracy may decrease over time as storage device behavior changes.
How does the method of generating global attribute information improve the accuracy of failure predictions in storage devices?
By considering past attribute information of storage devices within a specific time window, the method of generating global attribute information provides a historical context for failure prediction. This helps in identifying patterns or trends in storage device behavior that may lead to failures.
Original Abstract Submitted
a failure prediction apparatus and method for storage devices are provided, the method including: obtaining attribute information of a plurality of attributes for a plurality of storage devices during operation of a storage apparatus; obtaining global attribute information for each of the plurality of storage devices based on the attribute information of the plurality of attributes obtained within a first time window before the current time; and predicting failures for the plurality of storage devices using a trained machine-learning model based on attribute information of the plurality of attributes of each of the plurality of storage devices obtained within a second time window before the current time and the global attribute information of each of the plurality of storage devices.
