Disk Usage Growth Prediction System for Storage Capacity Management
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Solution Overview
Problem
Businesses face challenges in predicting when storage devices in information management systems will reach their maximum capacity, leading to risks of under-provisioning or over-provisioning, due to varying growth patterns and anomalous behaviors.
Innovation Solution
An improved disk usage growth prediction system that determines usage status data, performs validation checks using multiple prediction models, identifies the best performing model, generates a disk usage growth prediction, and adjusts available storage space accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If storage capacity is increased to prevent under-provisioning, then storage availability is improved, but storage cost increases
Solution Approach 1:
The system performs preliminary actions by continuously monitoring usage status data and generating disk usage growth predictions before storage capacity is actually reached. This allows proactive storage provisioning decisions to be made in advance, preventing both under-provisioning and over-provisioning by predicting future storage needs based on historical patterns and current trends.
2Measurement precision
If multiple prediction models are used to improve prediction accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system segments the prediction task by employing multiple specialized prediction models (e.g., linear regression, exponential growth, logistic growth models) that each handle different storage growth patterns. This segmentation allows each model to be optimized for specific scenarios while collectively providing comprehensive and accurate predictions across diverse storage behaviors.
Solution Approach 2:
The system changes parameters by adjusting model selection and prediction horizons based on the specific characteristics of each storage device and its usage patterns. Different models are applied to different devices depending on their growth patterns, and prediction timeframes are adjusted based on operational needs, allowing the system to maintain high accuracy without unnecessary complexity.
Data Source
AI summary
Certain embodiments described herein relate to an improved disk usage growth prediction system. In some embodiments, one or more components in an information management system can determine usage status data of a given storage device, perform a validation check on the usage status data using multiple prediction models, compare validation results of the multiple prediction models to identify the best performing prediction model, generate a disk usage growth prediction using the identified prediction model, and adjust the available space of the storage device according to the disk usage growth prediction.