Digital Twin Artificial Aging for Infrastructure Power Prediction
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Solution Overview
Problem
Managing infrastructure, particularly computing infrastructure, is challenging due to difficulties in replicating customer environments, predicting behavior, and accessing power consumption data, as vendors face limitations in accurately simulating customer deployments and customers are hesitant to share infrastructure details for security reasons.
Innovation Solution
The creation and artificial aging of digital twins to virtually represent infrastructure, allowing for the application of datasets to simulate current or future states, enabling the prediction of power consumption and informing actionable insights or workload adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If digital twins are created to represent infrastructure, then the ability to predict power consumption and analyze performance is improved, but the complexity of creating and maintaining virtual representations increases
Solution Approach 1:
The patent creates virtual copies (digital twins) of physical infrastructure components to simulate and predict their behavior. These digital twins replicate the functional characteristics of the physical systems, enabling power consumption analysis without directly accessing or modifying the actual infrastructure. This copying approach resolves the contradiction by providing accurate predictions through simplified virtual models rather than complex direct measurement systems.
Solution Approach 2:
The digital twin acts as an intermediary between the physical infrastructure and the analysis system. Instead of directly measuring or accessing the physical system, the patent uses the virtual representation as a mediator to predict power consumption and performance characteristics. This intermediary approach enables accurate predictions while avoiding the complexity of direct physical system integration.
2Loss of time
If datasets are applied to artificially advance digital twins to future states, then the ability to predict future power consumption is improved, but the computational resources and time required increase
Solution Approach 1:
The patent applies datasets to digital twins to artificially advance them through time, simulating future states and power consumption patterns. This preliminary action allows the system to predict future power consumption requirements before they actually occur, enabling proactive infrastructure management. The virtual time advancement compresses what would otherwise require years of real-time observation into computationally manageable simulations.
Solution Approach 2:
The patent changes the time parameter of the digital twin by applying datasets that accelerate the virtual representation through operational states. Instead of waiting for real-time progression, the system modifies the temporal parameter by applying workload datasets that simulate years of operation in minutes of computation, resolving the contradiction between prediction accuracy and computational cost.
3Ease of operation
If digital twins are used to simulate infrastructure behavior, then the ability to analyze performance without direct access is improved, but the difficulty of accurately replicating customer environments increases
Solution Approach 1:
The patent creates digital copies of infrastructure that capture the essential functional characteristics needed for power consumption analysis. Rather than attempting to perfectly replicate every aspect of the customer environment, the virtual representations focus on the critical parameters and operational behaviors that drive power consumption, making the system both accessible and sufficiently accurate.
Solution Approach 2:
The digital twin platform provides universal functionality for analyzing different types of infrastructure through a common virtual representation framework. This multi-functional approach allows the same system to handle various infrastructure types and customer environments, reducing the difficulty of replication by using standardized virtual models that can be adapted to different physical systems.
Data Source
AI summary
A method obtains at least one virtual representation of an infrastructure, wherein the virtual representation represents the infrastructure in a first state. The method applies a dataset to the virtual representation to artificially advance the virtual representation to represent the infrastructure in a second state. The method obtains results representing the infrastructure in the second state, responsive to applying the dataset to the virtual representation, wherein at least a portion of the results are indicative of a predicted power consumption associated with the infrastructure. The method initiates one or more actions with respect to the infrastructure in accordance with the predicted power consumption.