US-12619804-B2 - Artificial aging of digital twin to predict usage over time of infrastructure

Abstract

A method obtains at least one virtual representation representing an infrastructure in a first state and applies a first 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, wherein at least a portion of the results are indicative of a predicted condition associated with the infrastructure based on usage of the infrastructure. The method applies a second dataset to the virtual representation to artificially advance the virtual representation to represent the infrastructure in a third state and obtains results representing the infrastructure in the third state, wherein at least a portion of the results are indicative of an outcome to a solution applied via the second dataset to the predicted condition associated with the infrastructure.

Inventors

• Judith A. Furlong
• Aidan O Mahony
• Said Tabet
• Robert A. Lincourt, JR.

Assignees

• DELL PRODUCTS L.P.

Dates

Publication Date

20260505

Application Date

20230330

Claims (20)

1. 1 . A method, comprising: obtaining at least one virtual representation of an infrastructure which is physically deployed, wherein the virtual representation represents the infrastructure in a first state; applying a first dataset to the virtual representation of the infrastructure in the first state to artificially advance the virtual representation to represent the infrastructure in a second state and to predict a condition of usage associated with the infrastructure as represented by the virtual representation of the infrastructure in the second state; obtaining results representing the infrastructure in the second state, responsive to applying the first dataset to the virtual representation, wherein at least a portion of the results include a predicted condition of usage of the infrastructure based on usage of the infrastructure as represented by the virtual representation of the infrastructure in the second state; applying a second dataset to the virtual representation of the infrastructure in the second state to artificially advance the virtual representation to represent the infrastructure in a third state, the second dataset comprising a solution dataset which is applied to address the predicted condition of usage; obtaining results representing the infrastructure in the third state, responsive to applying the second dataset to the virtual representation, wherein at least a portion of the results include an outcome to the solution dataset applied to address the predicted condition of usage; and initiating one or more actions with respect to the infrastructure to address the predicted condition of usage, when the outcome to the solution dataset applied to address the predicted condition of usage is deemed successful; wherein the steps are performed by at least one processor and at least one memory storing executable computer program instructions.
2. 2 . The method of claim 1 , wherein the first state, the second state, and the third state of the infrastructure, which the virtual representation represents, corresponds to at least one of a workload execution schedule, a hardware configuration, a software configuration, and a data configuration of the infrastructure at a first time instance, a second time instance, and a third time instance, respectively.
3. 3 . The method of claim 2 , wherein the first time instance comprises a time instance prior to an occurrence of the predicted condition of usage, the second time instance comprises a time instance at or about the occurrence of the predicted condition of usage, and the third time instance comprises a time instance at or about a resolution of the predicted condition of usage.
4. 4 . The method of claim 1 , wherein the predicted condition of usage comprises at least one of a degradation and a downtime associated with the infrastructure.
5. 5 . The method of claim 1 , wherein the first dataset represents measurement data collected at the infrastructure, and wherein the measurement data comprises at least one or more workloads associated with the infrastructure.
6. 6 . The method of claim 5 , wherein applying the first dataset to the virtual representation of the infrastructure in the first state to artificially advance the virtual representation to represent the infrastructure in the second state further comprises simulating execution of the one or more workloads in the virtual representation.
7. 7 . The method of claim 6 , wherein applying the second dataset to the virtual representation of the infrastructure in the second state to artificially advance the virtual representation to represent the infrastructure in the third state further comprises modifying one or more of a configuration and a workload execution schedule represented by the virtual representation.
8. 8 . The method of claim 1 , wherein the virtual representation comprises a combination of a physics-based model and an artificial intelligence-driven model.
9. 9 . The method of claim 1 , wherein at least a portion of the results are further indicative of a cost associated with the solution dataset applied to address the predicted condition of usage.
10. 10 . The method of claim 1 , wherein the virtual representation comprises at least one digital twin.
11. 11 . An apparatus, comprising: at least one processor and at least one memory storing computer program instructions wherein, when the at least one processor executes the computer program instructions, the apparatus is configured to: obtain at least one virtual representation of an infrastructure which is physically deployed, wherein the virtual representation represents the infrastructure in a first state; apply a first dataset to the virtual representation of the infrastructure in the first state to artificially advance the virtual representation to represent the infrastructure in a second state and to predict a condition of usage associated with the infrastructure as represented by the virtual representation of the infrastructure in the second state; obtain results representing the infrastructure in the second state, responsive to applying the first dataset to the virtual representation, wherein at least a portion of the results include a predicted condition of usage of the infrastructure based on usage of the infrastructure as represented by the virtual representation of the infrastructure in the second state; apply a second dataset to the virtual representation of the infrastructure in the second state to artificially advance the virtual representation to represent the infrastructure in a third state, the second dataset comprising a solution dataset which is applied to address the predicted condition of usage; obtain results representing the infrastructure in the third state, responsive to applying the second dataset to the virtual representation, wherein at least a portion of the results include an outcome to the solution dataset applied to address the predicted condition of usage; and initiate one or more actions with respect to the infrastructure to address the predicted condition of usage, when the outcome to the solution dataset applied to address the predicted condition of usage is deemed successful.
12. 12 . The apparatus of claim 11 , wherein the first state, the second state, and the third state of the infrastructure, which the virtual representation represents, corresponds to at least one of a workload execution schedule, a hardware configuration, a software configuration, and a data configuration of the infrastructure at a first time instance, a second time instance, and a third time instance, respectively.
13. 13 . The apparatus of claim 12 , wherein the first time instance comprises a time instance prior to an occurrence of the predicted condition of usage, the second time instance comprises a time instance at or about the occurrence of the predicted condition of usage, and the third time instance comprises a time instance at or about a resolution of the predicted condition of usage.
14. 14 . The apparatus of claim 12 , wherein the predicted condition of usage comprises at least one of a degradation and a downtime associated with the infrastructure.
15. 15 . The apparatus of claim 12 , wherein the first dataset represents measurement data collected at the infrastructure, and wherein the measurement data comprises at least one or more workloads associated with the infrastructure.
16. 16 . The apparatus of claim 15 , wherein applying the first dataset to the virtual representation of the infrastructure in the first state to artificially advance the virtual representation to represent the infrastructure in the second state further comprises simulating execution of the one or more workloads in the virtual representation.
17. 17 . The apparatus of claim 16 , wherein applying the second dataset to the virtual representation of the infrastructure in the second state to artificially advance the virtual representation to represent the infrastructure in the third state further comprises modifying one or more of a configuration and a workload execution schedule represented by the virtual representation.
18. 18 . The apparatus of claim 11 , wherein the virtual representation comprises a combination of a physics-based model and an artificial intelligence-driven model.
19. 19 . The apparatus of claim 11 , wherein at least a portion of the results are further indicative of a cost associated with the solution dataset applied to address the predicted condition of usage.
20. 20 . A computer program product stored on a non-transitory computer-readable medium and comprising machine executable instructions, the machine executable instructions, when executed, causing a processing device to perform steps of: obtaining at least one virtual representation of an infrastructure which is physically deployed, wherein the virtual representation represents the infrastructure in a first state; applying a first dataset to the virtual representation of the infrastructure in the first state to artificially advance the virtual representation to represent the infrastructure in a second state and to predict a condition of usage associated with the infrastructure as represented by the virtual representation of the infrastructure in the second state; obtaining results representing the infrastructure in the second state, responsive to applying the first dataset to the virtual representation, wherein at least a portion of the results include a predicted condition of usage of the infrastructure based on usage of the infrastructure as represented by the virtual representation of the infrastructure in the second state; applying a second dataset to the virtual representation of the infrastructure in the second state to artificially advance the virtual representation to represent the infrastructure in a third state, the second dataset comprising a solution dataset which is applied to address the predicted condition of usage; obtaining results representing the infrastructure in the third state, responsive to applying the second dataset to the virtual representation, wherein at least a portion of the results include an outcome to the solution dataset applied to address the predicted condition of usage; and initiating one or more actions with respect to the infrastructure to address the predicted condition of usage, when the outcome to the solution dataset applied to address the predicted condition of usage is deemed successful.

Description

FIELD The field relates generally to infrastructure environments, and more particularly to virtual representations (e.g., digital twins) in such infrastructure environments (e.g., computing environment). BACKGROUND Recently, techniques have been proposed to attempt to represent infrastructure in a computing environment so as to more efficiently manage the infrastructure including attributes and operations associated with the infrastructure. One proposed way to represent the infrastructure is through the creation of a digital twin architecture. A digital twin typically refers to a virtual representation (e.g., virtual copy) of a physical (e.g., actual or real) product, process, and/or system. By way of example, a digital twin can be used to analyze the performance of a physical product, process, and/or system in order to better understand operations associated with the product, process, and/or system being virtually represented. However, utilization of digital twins for various types of infrastructure can be a significant challenge. SUMMARY Embodiments provide automated management techniques associated with virtual representations that represent infrastructure. For example, according to one illustrative embodiment, 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 first 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 first dataset to the virtual representation, wherein at least a portion of the results are indicative of a predicted condition of the infrastructure based on usage of the infrastructure. The method applies a second dataset to the virtual representation to artificially advance the virtual representation to represent the infrastructure in a third state. The method obtains results representing the infrastructure in the third state, responsive to applying the second dataset to the virtual representation, wherein at least a portion of the results are indicative of an outcome to a solution applied via the second dataset to the predicted condition. The method initiates one or more actions with respect to the infrastructure to address the predicted condition, when the solution applied via the second dataset to the predicted condition is successful. Further illustrative embodiments are provided in the form of a non-transitory computer-readable storage medium having embodied therein executable program code that when executed by a processor causes the processor to perform the above steps. Additional illustrative embodiments comprise an apparatus with a processor and a memory configured to perform the above steps. Advantageously, illustrative embodiments provide functionalities for artificially aging (advancing) a virtual representation (e.g., digital twin) of an infrastructure (e.g., a computing infrastructure). Among other advantages, by way of example only, such artificial aging of a digital twin enables an understanding of a current condition of the infrastructure when access to the infrastructure may be limited or otherwise unavailable, and/or an understanding of a condition of the infrastructure. Based on results generated in accordance with the digital twin, one or more actions can be initiated with respect to the condition(s) of the infrastructure. These and other features and advantages of embodiments described herein will become more apparent from the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 illustrates a digital twin environment according to an illustrative embodiment. FIG. 2 illustrates a computing environment with digital twin management according to an illustrative embodiment. FIG. 3A illustrates an exemplary process of artificially aging a digital twin according to an illustrative embodiment. FIG. 3B illustrates an exemplary process of artificially aging a digital twin to predict usage and a condition over time of an infrastructure according to an illustrative embodiment. FIG. 4 illustrates a methodology for artificially aging a digital twin to predict usage and a condition over time of an infrastructure according to an illustrative embodiment. FIGS. 5 and 6 illustrate examples of processing platforms that may be utilized to implement at least a portion of an information processing system with digital twin management functionality according to one or more illustrative embodiments. DETAILED DESCRIPTION Illustrative embodiments will be described herein with reference to exemplary information processing systems and associated computers, servers, storage devices and other processing devices. It is to be appreciated, however, that embodiments are not restricted to use with the particular illustrative system and dev