Adaptive File System Capacity Risk Analysis
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Solution Overview
Problem
Current methods for analyzing the risk of reaching maximum file system capacity often result in false positives, particularly when utilization patterns change, leading to unnecessary system updates and costs.
Innovation Solution
A method using linear regression to project future file system utilization, calculating the rate of change, and determining the variation of these changes across time intervals to accurately assess the risk of reaching maximum capacity, thereby reducing false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If linear regression analysis is used to predict file system capacity risk, then the analysis can be performed automatically, but false positives increase when utilization patterns change
Solution Approach 1:
The patent applies dynamics by making the analysis period adaptive rather than fixed. The system dynamically adjusts the lookback period based on the rate of change in utilization patterns - using shorter periods when utilization is stable and longer periods when utilization is changing rapidly. This allows the linear regression analysis to remain automated while adapting to changing patterns to reduce false positives.
Solution Approach 2:
The patent changes the parameter of the analysis period (lookback period) based on observed utilization patterns. By monitoring the rate of change in utilization and adjusting the time window accordingly, the system maintains prediction accuracy while preserving automation. This parameter adaptation resolves the contradiction between automated analysis and prediction precision.
2Device complexity
If a fixed analysis period is used for linear regression, then the analysis is simple and automated, but it produces false positives when utilization patterns change over time
Solution Approach 1:
The system transitions from a static fixed analysis period to a dynamic adaptive period. The analysis period automatically adjusts based on the observed rate of change in utilization patterns, maintaining simplicity and automation while improving accuracy by using appropriate time windows for different utilization scenarios.
Solution Approach 2:
The system implements feedback by monitoring the rate of change in utilization patterns and using this information to adjust the analysis period. This feedback loop allows the system to automatically adapt to changing conditions, reducing false positives while maintaining low complexity through rule-based adjustments rather than complex algorithms.
3Measurement precision
If the analysis period is extended to capture more data, then prediction accuracy may improve, but the response time to detect actual capacity risks increases
Solution Approach 1:
The system dynamically adjusts the analysis period length based on the rate of change in utilization. When utilization is changing rapidly, a shorter period is used to enable faster detection. When utilization is stable, a longer period can be used to improve prediction accuracy. This dynamic adjustment resolves the trade-off between accuracy and detection time.
Solution Approach 2:
The analysis period parameter is changed adaptively based on observed patterns. By monitoring the rate of change and adjusting the time window accordingly, the system optimizes the balance between having enough data for accurate predictions and responding quickly to actual capacity risks.
Data Source
AI summary
Systems and methods for automated file system capacity risk analysis include obtaining first utilization data of the file system during a plurality of series of time intervals, projecting a future utilization value for the series of time intervals, and determining a threshold utilization percentage indicative of a risk of reaching maximum capacity of the file system. In response to the projected future utilization value being equal to or greater than the threshold, calculating a rate of change of the first utilization data for each of the series of time intervals, determining a variation of the rates of change of all the series of time intervals, and in response to the variation of the rates of change being positive or the first utilization data for the last time interval being equal to or greater than the threshold, designating the file system as being at risk of reaching maximum capacity.