Cloud Storage Migration Script Generation
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Solution Overview
Problem
The manual and tedious process of configuring files for migration to cloud storage is costly and time-consuming, requiring human operators to define settings for each migration step-by-step, which is inefficient and resource-intensive.
Innovation Solution
A system that utilizes pre-configured configuration information to automatically convert files into a migration dataset and generate scripts for cloud storage, leveraging computing modules to securely format and encode data, thereby automating the migration process to cloud storage services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual configuration is used for each migration step, then migration can be performed with basic tools, but the process becomes tedious and expensive requiring multiple human operators
Solution Approach 1:
The patent applies preliminary action by pre-configuring migration settings, parameters, and scripts before the actual migration process. Configuration files are created in advance with all necessary migration parameters, and automated scripts are prepared beforehand to execute the migration steps without requiring manual configuration during the migration itself. This eliminates the need for operators to define settings step-by-step during migration execution.
Solution Approach 2:
The system applies self-service by enabling automated migration processes that execute without continuous human intervention. Once the configuration files are prepared, the migration system automatically performs data conversion, script generation, and migration execution. The system serves itself by using pre-defined parameters and automated workflows to complete the entire migration process, reducing dependency on multiple human operators.
2Quantity of substance
If manual step-by-step configuration is performed, then basic computing resources are sufficient, but the process is time-consuming and resource-intensive
Solution Approach 1:
The patent applies mechanics substitution by replacing manual mechanical operations with automated computational processes. Instead of human operators manually configuring each migration step, the system uses automated scripts and programming to perform data conversion, parameter validation, and migration execution. This substitution of manual mechanical configuration with automated computational processes significantly reduces both time and human resource requirements.
Solution Approach 2:
The system applies parameter changes by transforming migration from a manual process with human-defined parameters to an automated process with pre-configured parameters. Configuration files contain all necessary parameters (source/destination paths, data formats, migration settings) that are automatically applied during migration. This parameterization enables the system to execute migrations quickly without requiring human operators to define and adjust parameters during the process.
3Productivity
If automated migration is implemented, then efficiency improves and costs reduce, but configuration information and settings must be pre-defined
Solution Approach 1:
The patent applies segmentation by dividing the migration configuration into separate, modular configuration files. Each configuration file contains specific parameters for different aspects of the migration (source data specifications, destination storage parameters, conversion settings). This segmentation allows the system to process different migration aspects independently and systematically, improving efficiency while maintaining organized configuration structures that are easier to manage than monolithic configuration systems.
Data Source
AI summary
Techniques to facilitate a migration process of source data to cloud storage are described. These techniques use configuration information with pre-configured settings for the migration process by leveraging such information to build a component to execute the migration process. These settings can be used to identify computing modules (including interfaces) for generating a script for loading the source data to a storage location managed by a cloud storage service. The script may rely upon a data model for organizing the source data, which also is provided in the settings. Once the source data is available, the source data is converted into a suitable migration dataset and communicated with the script to the cloud storage service in a single operation. Other embodiments are described and claimed.