Cloud Storage Data Protection via Predictive Failure Modeling
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Solution Overview
Problem
Existing data protection methods for cloud-based service systems face challenges in predicting storage device failures, particularly sudden failures due to workload demands, and do not adequately account for the influence of workloads on storage device lifespan, leading to potential performance degradation or system collapse.
Innovation Solution
A method that collects historical operating data to build a life expectancy model and a next-7-days failure probability model using ANN algorithms, considering performance data and SMART data, to predict storage device lifespans and failure probabilities, and schedules data backups based on these predictions, using a fuzzy system to determine snapshot intervals for backup frequency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If frequent data back-ups are processed, then data protection reliability is improved, but workload performance is degraded
Solution Approach 1:
The patent dynamically adjusts backup frequency by changing the parameter of backup interval based on predicted storage device lifespan and failure probability. When storage devices are healthy, backup frequency is reduced; when failure risk increases, backup frequency is increased, thus optimizing both data protection reliability and workload performance
Solution Approach 2:
The system transitions from static fixed-interval backups to dynamic adaptive backups. The backup frequency automatically adapts based on real-time monitoring of storage device health indicators and predicted failure probabilities, allowing the system to optimize between protection and performance based on current conditions
2Device complexity
If traditional lifespan prediction models are used, then prediction process is simple, but prediction precision is insufficient for sudden failures
Solution Approach 1:
The system performs preliminary monitoring and prediction of storage device failure risks before actual failures occur. By continuously analyzing health indicators and predicting failure probabilities, the system can prepare backup strategies in advance, preventing data loss rather than reacting after failure
Solution Approach 2:
The patent implements a feedback mechanism where prediction results are continuously monitored and used to adjust backup strategies. The system feeds back the predicted failure probabilities and actual device states to refine prediction models and optimize backup frequency dynamically
Data Source
AI summary
A method for data protection in a cloud-based service system is disclosed. The method includes the steps of: A. collecting historical operating data of storage devices in the cloud-based service system; B. building up a life expectancy model and a next-7-days failure probability model by the collected operating data; C. inputting operating data in the past 24-hours into the life expectancy model and the next-7-days failure probability model for every storage device to obtain ranges of expected lifespans in respective groups and corresponding failure probabilities; and D. backing up data in the storage devices according to the results of step C.