Database Consolidation via Processor-Based Size Classification
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Solution Overview
Problem
Database migration is complex due to varying hardware configurations and management systems, stringent functionality requirements, and organizational constraints, making efficient data migration a longstanding challenge.
Innovation Solution
Classifying source databases by predetermined sizes based on processor information and mapping them to target database hardware segmented into containers, allowing for efficient data migration while adhering to parameters like security zones and processor information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If databases are classified and mapped to target hardware containers based on processor information and predetermined sizes, then migration efficiency and resource usage are improved, but the complexity of the migration process increases due to classification and mapping operations
Solution Approach 1:
The patent segments source databases into predetermined size classes based on processor information and maps them to corresponding target hardware containers. This segmentation allows systematic organization of databases by size and enables automated matching to appropriate hardware resources, improving migration efficiency while managing complexity through structured classification.
Solution Approach 2:
The patent uses processor information as a key parameter to classify databases into different size categories. By changing the classification parameter from generic database listing to processor-based size classification, the system achieves more efficient resource allocation and automated mapping to target hardware containers.
2Use of energy by moving object
If databases are classified into predetermined sizes and mapped to segmented target hardware, then resource usage is optimized, but the time required for classification and mapping operations increases
Solution Approach 1:
The patent performs preliminary classification of source databases into predetermined size classes based on processor information before the actual migration process. This preliminary action organizes databases in advance by size categories, enabling automated and efficient mapping to target hardware containers during migration, thus optimizing resource usage while minimizing time consumption through pre-organization.
3Reliability
If source databases are classified and mapped to target hardware based on multiple parameters including security zones and processor information, then migration accuracy and compliance are improved, but the complexity of parameter validation and compliance checking increases
Solution Approach 1:
The patent segments the migration process into parameter-based classification stages, where databases are organized by security zones, processor information, and predetermined sizes. This segmentation allows systematic validation of each parameter category separately, ensuring compliance while managing the complexity of multi-parameter validation through structured approach.
Data Source
AI summary
Embodiments include systems and methods for performing data migration using database consolidation. Information and parameters about a plurality of source databases from a source system can be stored, the parameters including a location, a security zone, and processor information for the source databases. Each of the plurality of source databases can be classified to one of a plurality of predetermined database sizes based on the stored information and parameters, wherein the classifying is at least based on the processor information. The classified source databases can be mapped to target database hardware based on the classified sizes and the stored parameters, wherein the target database hardware is segmented into containers that are defined by one or more of the parameters. Data from the source databases can be migrated to the target database hardware based on the mappings.