Database Provisioning Recommendations With Predictive Resource Management
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Solution Overview
Problem
Existing database management systems are inefficient, requiring manual user interaction, are vendor-specific, and lack predictive capabilities for resource adjustments and event predictions, leading to unsuitable database selection, increased costs, and performance issues.
Innovation Solution
A centralized platform that automatically provisions and manages various database types using machine learning models to predict events and adjust resources, generating recommendations based on data type, access patterns, and workload characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual intervention from database administrators is used for database provisioning and management, then customization and control are improved, but productivity and efficiency deteriorate
Solution Approach 1:
The system enables self-service database provisioning through automated workflows where the database management system automatically provisions, configures, and manages databases based on application requirements without requiring manual database administrator intervention for routine operations
Solution Approach 2:
The system performs preliminary actions by pre-configuring database templates, access patterns, and workload characteristics before actual database provisioning occurs, allowing automated decision-making and reducing the need for manual customization during deployment
2Reliability
If vendor-specific database management systems are used, then database-specific optimization is improved, but adaptability and versatility deteriorate
Solution Approach 1:
The system provides universal database management capabilities that work across multiple database types and vendors through a unified interface, supporting relational, NoSQL, and other database types while maintaining consistent management operations and performance optimization
Solution Approach 2:
The system acts as an intermediary layer between applications and various database systems, translating application requirements into database-specific configurations and management operations, thereby providing vendor-agnostic control while maintaining database-specific optimization
3Device complexity
If traditional database management approaches are used, then simplicity is improved, but detection precision of performance issues deteriorates
Solution Approach 1:
The system implements continuous feedback mechanisms by monitoring database performance metrics, access patterns, and workload characteristics in real-time, automatically detecting performance issues and triggering corrective actions while maintaining manageable system complexity through standardized monitoring templates
4Productivity
If automated database provisioning is implemented, then productivity is improved, but device complexity deteriorates
Solution Approach 1:
The system segments database provisioning into distinct modular components including requirement analysis, template selection, automated configuration, and deployment stages, allowing each component to be independently managed and reducing overall system complexity while maintaining high automation capability
Data Source
AI summary
A system and methods for provisioning and managing databases are disclosed. A request is received to provision a database for a software application, including a general database type and one or more parameters for the requested database. Based on the received request, the system generates database recommendations including a recommended specific type of database. The system receives a selection of one of the recommended databases and determines whether a user submitting the request is approved, based on a level of privileges or a quota. When the user is approved for the selected database, the system generates the selected databased by retrieving database code from a repository. The system updates an inventory to reflect the provisioning of the generated database. The generated database can then be managed using the system, including predicting adverse events associated with the generated database and/or predicting a need to adjust resources for the generated database.