PaaS Automation Engine for Digital Twin Generation
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Solution Overview
Problem
Existing digital twin generation methods are limited to homogenous computing environments, rely on manual processes, and negatively impact production system performance and availability, lacking flexibility and scalability.
Innovation Solution
A PaaS automation engine automates the generation of digital twins by accessing parameter information, replicating application programming, and migrating it to a digital twin staging store, while identifying and updating deltas, ensuring target hosts have sufficient capacity and no conflicts, supporting heterogeneous environments and multi-cloud migrations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual digital twin generation processes are used, then homogenous computing environments can be replicated, but the process is labor-intensive and prone to human error
Solution Approach 1:
The system enables self-service automation where the digital twin generation process automatically discovers source system configurations, replicates environments, and manages updates without manual intervention. The automated engine performs parameter extraction, system replication, and delta identification autonomously, eliminating human error while maintaining high reliability.
Solution Approach 2:
Manual mechanical processes of system analysis and replication are replaced with an automated digital engine that uses software-based parameter extraction and system cloning. The automated engine substitutes human operators with algorithmic processes that systematically replicate computing environments with higher precision and consistency.
2Quantity of substance
If digital twin generation is performed using traditional methods, then system replication can be achieved, but production system performance and availability are negatively impacted
Solution Approach 1:
The digital twin generation process is segmented into independent phases: parameter extraction, system replication, delta identification, and update application. This segmentation allows the process to be performed in controlled stages that minimize disruption to production systems, enabling twin generation without compromising production availability.
Solution Approach 2:
An automated engine acts as an intermediary between the source production system and the digital twin environment. This intermediary coordinates data extraction and replication processes, managing the interface between production and twin systems to ensure that twin generation operations do not interfere with production system performance or availability.
3Stability of the object's composition
If manual digital twin updates are performed, then system synchronization can be achieved, but the process is time-consuming and inefficient
Solution Approach 1:
The automated engine implements feedback mechanisms that continuously monitor differences between source systems and digital twins. Delta identification processes automatically detect changes and trigger synchronized updates, ensuring system composition stability while maintaining high update speed through automated detection and correction cycles.
Solution Approach 2:
The update process operates continuously rather than in discrete manual steps. The automated engine maintains continuous synchronization by constantly monitoring for deltas and immediately applying updates, eliminating idle time between detection and correction actions, thereby achieving both stability and high productivity.
4Reliability
If digital twin generation is limited to homogenous environments, then system compatibility is maintained, but flexibility and scalability are reduced
Solution Approach 1:
The automated engine is designed with universal capabilities that enable it to handle multiple computing environments (homogenous and heterogeneous) through a single unified process. The system can replicate diverse environments including different operating systems, database platforms, and application stacks, providing both compatibility assurance and environmental flexibility through multi-functional automation.
Solution Approach 2:
The system manages environmental diversity by dynamically adjusting parameters during the replication process. It automatically adapts to different source system configurations, modifying replication parameters based on the specific environment being cloned, thereby maintaining compatibility across heterogeneous systems while enabling broad environmental support.
Data Source
AI summary
A platform as a service (“PaaS”) automation engine receives a request to generate a digital twin of a source computing system. The source computing system is mounted for on demand access to at least part of the source computing system. Parameter information representing aspects of the source computing system and the digital twin is accessed and application programming comprised in the source computing system is replicated. The replicated application programming is migrated to a digital twin staging store associated with the digital twin and provided to the digital twin. A delta between the replicated application programming and the source computing system is identified and the application programming of the digital twin is updated. Data associated with at least part of the source computing system are provided to the digital twin and the source computing system is unmounted.