Digital Twin Component Staging for Low-Downtime Reconfiguration
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Solution Overview
Problem
Existing digital twin configurations face inefficiencies due to the computational expense and resource constraints of pre-loading all components, leading to sub-optimal performance and downtime during reconfiguration.
Innovation Solution
An orchestration process that adaptively unstages and preempts the staging of digital twin components, optimizing resource use and minimizing downtime by compressing components into available storage and deploying them as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If all DT components are pre-loaded and always executing in the computational infrastructure, then the DT can quickly respond to operational questions, but the computational resources and memory consumption increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-loading only the essential core components of the digital twin that are frequently needed, while keeping less frequently used components unloaded. This allows the system to maintain fast response times for common operations without consuming excessive memory resources.
Solution Approach 2:
The digital twin components are segmented into different priority levels and loading groups. Critical components are pre-loaded into memory, while non-critical components remain unloaded or stored externally. This segmentation allows the system to optimize the balance between response speed and memory consumption by loading only necessary segments.
2Quantity of substance
If DT components are staged and unstaged dynamically based on application needs, then resource utilization is optimized, but the reconfiguration process introduces downtime
Solution Approach 1:
The system performs preliminary staging of DT components in advance based on predicted application needs and usage patterns. By anticipating which components will be needed next and pre-loading them before they are actually required, the system minimizes reconfiguration downtime while maintaining optimized resource utilization.
Solution Approach 2:
The system implements feedback mechanisms that monitor current DT component usage patterns and performance metrics. This feedback information is used to dynamically adjust the staging/unstaging decisions, allowing the system to optimize resource utilization while minimizing downtime by learning from actual usage behavior.
3Quantity of substance
If less frequently used DT components are kept unloaded to save resources, then memory consumption is reduced, but the DT may experience delays when those components are needed
Solution Approach 1:
The system performs preliminary loading of less frequently used DT components based on predicted future needs, usage patterns, and application context. By anticipating which unloaded components will be needed soon and pre-loading them in background operations, the system reduces the actual loading delays when those components are required, while still maintaining lower memory consumption compared to loading everything upfront.
Data Source
AI summary
One example method includes receiving a DTC (digital twin configuration) that is configured to perform a predefined task, and the DTC comprising component sets, when it is determined that models of one of the component sets can be executed simultaneously, staging the models, beginning execution of the one component set, and calculating available resources, and, when the available resources are below a threshold, waiting until the one component set is executed, and then unstaging one or more of the models of the one component set, and when the available resources are at or above the threshold, staging a model of a next one of the component sets.