Database Storage Prediction via Application Operation Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current database management systems lack an effective means to accurately predict storage requirements, leading to potential performance degradation or failure due to insufficient storage planning, especially with the exponential increase of data from various sources.
Innovation Solution
A method that monitors storage-related operations of applications connected to the database management system, categorizes them based on similar operations, and builds a mathematical model to forecast storage requirements, ensuring adequate storage capacity is allocated.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional storage monitoring methods are used, then storage capacity is maintained, but storage requirements cannot be accurately predicted leading to performance degradation or failure
Solution Approach 1:
The system performs preliminary actions by continuously monitoring storage-related operations and building predictive models in advance, before storage capacity is actually exhausted. This allows the system to forecast future storage requirements and take preventive measures, avoiding the reactive approach of waiting for storage exhaustion to occur.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual storage operations and comparing predicted versus actual storage usage. This feedback loop allows the predictive model to be refined and adjusted over time, improving prediction accuracy while maintaining a manageable level of complexity through automated adjustments.
2Measurement precision
If detailed monitoring of all application operations is performed, then accurate storage prediction is achieved, but system complexity and resource consumption increase
Solution Approach 1:
The system applies segmentation by categorizing applications into groups based on their storage operation patterns and characteristics. Instead of monitoring every application individually with equal detail, the system segments them into categories (e.g., read-intensive, write-intensive, batch processing) and applies appropriate monitoring strategies to each segment, reducing overall complexity while maintaining prediction accuracy.
Solution Approach 2:
The system implements partial monitoring by focusing on key storage-related operations that have the most significant impact on storage growth. Rather than monitoring every possible operation with equal depth, it selectively monitors the most influential operations (such as data inserts, updates, and deletions) while using modeling techniques to infer the impact of less critical operations, thereby reducing monitoring overhead.
Data Source
AI summary
A method, system and computer program product for forecasting a storage requirement of a database management system (DBMS). The storage-related operations (e.g., create, delete, update) of the applications connected to the DBMS are monitored. The impact on the storage usage of the DBMS based on these storage-related operations performed by the applications is monitored. Furthermore, the applications are categorized into groups of applications based on the monitored storage-related operations. A mathematical model is then built to forecast the storage requirement of the DBMS based on the monitored impact on the storage usage of the DBMS by the monitored storage-related operations of the applications and the categorization of the applications. The storage requirement of the DBMS is then forecasted based on the built mathematical model. In this manner, the storage requirements of the DBMS may be accurately predicted to ensure that there is available storage thereby preventing performance degradation.