Automation Management Sandbox for Predicting Status Change Effects
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Solution Overview
Problem
In complex IT environments, predicting the effects of interventions on managed computing resources is challenging due to system dependencies and the lack of deterministic methods to foresee the outcomes of commands like starting or stopping automated resources, making it difficult for human operators to understand the resulting state of the environment.
Innovation Solution
A system and method using a second system automation management system as a functional duplicate to simulate interventions, sending initial state data and status change commands, determining predicted response vectors, and providing derived actions to predict the outcomes without affecting the real environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a functional duplicate system is used to simulate interventions, then prediction accuracy of intervention effects is improved, but device complexity increases
Solution Approach 1:
The patent creates a functional duplicate (digital twin) of the automation management system that copies the structure, state, and behavior of the original system. This copy is used to simulate interventions and predict their effects without affecting the real system. The digital twin maintains synchronized state data and dependency relationships, enabling accurate prediction while isolating the complexity of the simulation from the operational system.
2Reliability
If comprehensive system state data is collected and processed, then prediction reliability is improved, but loss of time increases
Solution Approach 1:
The system pre-processes and stores comprehensive state data, dependency relationships, and system configurations in the digital twin before interventions occur. By having this information readily available in advance, the system can quickly perform predictions without time-consuming data collection during the actual prediction process, thus maintaining both reliability and speed.
3Stability of the object's composition
If a digital twin is used for simulation, then system stability is improved, but device complexity increases
Solution Approach 1:
The digital twin acts as an intermediary between the operational automation management system and the simulation process. All predictions and simulations are performed in the digital twin, which mediates between the real system and the analysis processes. This intermediary approach protects the stability of the operational system while enabling comprehensive simulations, as the complexity of the digital twin is isolated from the production system.
Data Source
AI summary
A computer-implemented method for predicting an effect of an intervention on managed computing resources using a first system automation management system, comprising an automated operations controller and at least one automation agent is disclosed. The method comprises sending initial state data of the first system automation management system to a second system automation management system which is a functional duplicate of the first system automation management system, sending a status change command and a related expected response vector, equivalent to a result of the intervention to the second system automation management system, determining, by the second system automation management system, a predicted response vector of the managed computing resources in response to the received status change command, and responding, by the second system automation management system, with the determined response vector and a set of predicted actions derived therefrom.