Database Storage Forecasting for Overallocation and Abend Prevention

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Solution Overview

Problem

Database systems face challenges in managing storage allocation due to varying data loads, leading to sudden surges that can cause failure, data loss, and excessive resource usage, requiring significant computational and manual intervention.

Innovation Solution

A computing system uses machine learning models to forecast storage needs based on database state data, determining average and peak usage thresholds to allocate or deallocate storage, optimizing resource use and preventing abnormal terminations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If storage allocation is increased to handle sudden data surges, then database reliability is improved, but storage resource waste occurs when data loads are low

Engineering Contradiction:

Improvedatabase reliabilityVSAvoidstorage resource waste

Core Design Contradiction:

ReliabilityVSLoss of substance

Solution Approach 1:

The system dynamically adjusts storage allocation based on real-time data load conditions and predictive analytics. Machine learning models continuously analyze storage consumption patterns and automatically provision or deprovision storage capacity, transitioning from static to dynamic allocation to match actual demand while preventing both waste and insufficiency

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system performs preliminary storage provisioning based on predictive analytics before data surges occur. Machine learning models forecast upcoming storage requirements and pre-allocate capacity, ensuring database reliability during sudden data loads without requiring permanent over-provisioning of storage resources

Inventive Principle:

Principle #10Preliminary action

2Measurement precision

If manual intervention is increased to accurately manage storage allocation, then storage management precision is improved, but operational complexity and time consumption increase

Engineering Contradiction:

Improvestorage management precisionVSAvoidtime consumption

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system implements self-service storage management where machine learning models automatically monitor storage consumption, predict future requirements, and execute provisioning decisions without human intervention. The autonomous system continuously learns from historical data and makes real-time adjustments, eliminating manual operational overhead while maintaining high precision in storage management

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system establishes continuous feedback loops where storage consumption data is collected, analyzed by machine learning models, and used to automatically adjust allocation decisions. This closed-loop control system refines its predictions over time based on actual outcomes, achieving high precision storage management through automated feedback-driven adjustments rather than manual intervention

Inventive Principle:

Principle #23Feedback

3Measurement precision

If computational resources are increased for manual storage evaluation, then storage forecasting accuracy is improved, but resource efficiency deteriorates

Engineering Contradiction:

Improvestorage forecasting accuracyVSAvoidcomputational resource efficiency

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system replaces manual computational evaluation with automated machine learning models that efficiently process storage data. These models use optimized algorithms to analyze patterns and predict storage requirements with high accuracy while consuming fewer computational resources than manual methods, substituting human-driven processes with automated intelligent systems

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12596694B2Collaborative automated system for intelligent storage forecasting and abend handling

Publication Date: 2026.04.07 BANK OF AMERICA CORP

AI summary

Aspects of the disclosure relate to using machine learning models to automatically deallocating overallocated database storage. A computing system may retrieve database state data that indicates current states of databases. Further, the computing system may retrieve average storage usage thresholds and peak storage usage threshold for the databases. The average storage threshold for the databases may indicate an average amount of storage used by each database. The peak storage usage threshold for the databases may indicate an amount of storage that is allocatable without causing an abnormal termination. Based on inputting the database state data, the average storage usage threshold, and the peak storage usage threshold into the machine learning models, an amount of overallocated storage of the databases may be determined. Further, the overallocated storage may be deallocated from the databases based on configurations of each of the databases.