Digital Twin Auto-Coding Orchestrator for Software Migration
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Solution Overview
Problem
Current methods for updating and migrating software across a network are time-consuming, resource-intensive, and often lead to security and network issues due to the presence of disparate versions and locations.
Innovation Solution
The use of a digital twin auto-coding orchestrator to automatically generate and implement update and migration workflows and code, minimizing downtime and disruption by analyzing infrastructure and older version data to create a digital twin and generate optimized workflows and machine-readable code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated update and migration workflows are implemented, then productivity and efficiency are improved, but device complexity increases
Solution Approach 1:
The patent introduces an orchestrator as an intermediary component that coordinates between the digital twin system and the actual software update/migration processes. The orchestrator receives infrastructure data, manages digital twin creation, generates workflows, and implements updates, thereby automating complex operations without requiring direct complex interactions between all system components.
Solution Approach 2:
The patent creates digital twins as virtual copies of the actual software systems. These digital twins replicate infrastructure data, software versions, and system states, allowing workflows to be designed, tested, and optimized in the virtual environment before being applied to real systems. This copying mechanism simplifies the complexity by working with replicas rather than directly managing the complex real systems.
2Manufacturing precision
If comprehensive infrastructure data is collected and analyzed, then manufacturing precision of workflows is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary actions by collecting and analyzing infrastructure data before actual software updates or migrations are executed. The system gathers data about current software versions, infrastructure characteristics, and system states, then uses this information to pre-generate optimized workflows in the digital twin environment. This preliminary analysis ensures high precision in workflow generation while the automated nature minimizes the time penalty.
Solution Approach 2:
The orchestrator and digital twin system perform self-service by automatically collecting, analyzing, and generating workflows without requiring manual intervention. The system autonomously processes infrastructure data, creates digital twins, designs workflows, and implements updates, significantly reducing the time loss associated with manual data collection and analysis while maintaining high precision.
3Reliability
If digital twin testing is performed before implementation, then reliability is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary testing of workflows in the digital twin environment before implementing them on actual systems. The digital twin replicates the target system's infrastructure and software state, allowing workflows to be validated, optimized, and tested for potential issues before real-world deployment. This preliminary action ensures high reliability by catching problems early while the automated testing process minimizes time loss.
Solution Approach 2:
The system uses digital twins as virtual copies to perform testing without affecting real systems. By replicating the target environment, the patent enables comprehensive workflow validation, performance testing, and risk assessment in isolation. This copying approach ensures reliability through thorough testing while avoiding the time loss that would result from testing on production systems or requiring system downtime.
Data Source
AI summary
Apparatus and methods to automatically create and implement update and migration workflows are provided. A digital twin auto-coding orchestrator may receive an updated version of a software program. The auto-coding orchestrator may gather infrastructure hardware data and version data. The auto-coding orchestrator may analyze the data. The auto-coding orchestrator may generate a workflow and code to update the older version to the updated version with the least amount of disruption and downtime. The auto-coding orchestrator may implement the workflow and update the software program to the updated version.


