Digital Twin Meta-Learning for Cross-Cloud AIOps Drift Adaptation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing AIOps models struggle with seamless migration across different cloud environments due to distribution drifts caused by varying compute, network, storage, and hardware configurations, leading to inefficiencies and delays in data collection and model adaptation.
Innovation Solution
A framework integrating digital twins for data generalization and meta-learning for model generalization, enabling the creation of a robust AIOps model that adapts to new environments with minimal to zero-shot observations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AIOps models are trained on data from specific cloud configurations, then model performance is optimized for that configuration, but the model cannot generalize to other cloud environments due to distribution drift
Solution Approach 1:
The patent creates digital twins of cloud configurations before actual migration occurs. These digital twins simulate various cloud environments (AWS, Azure, Google Cloud, on-premise) in advance, allowing the AIOps model to be pre-trained on diverse virtualized cloud data distributions. This preliminary action enables the model to learn multiple cloud configurations without requiring actual deployment data yet, resolving the contradiction between adaptability and reliability.
Solution Approach 2:
The patent uses digital twins as virtual copies of actual cloud configurations. Instead of training on real cloud data from specific providers, the system creates replicated virtual versions of different cloud environments. These copies allow the model to learn from multiple cloud architectures simultaneously while maintaining controlled training conditions, enabling generalization without sacrificing performance consistency.
2Productivity
If AIOps models are retrained for each new cloud environment, then model accuracy is maintained, but migration time and computational resources increase significantly
Solution Approach 1:
The system performs preliminary training on digital twins that represent multiple cloud environments before actual migration occurs. By pre-training the AIOps model on virtualized representations of different cloud providers and configurations, the model learns to recognize and adapt to various cloud architectures in advance. This eliminates the need for time-consuming retraining during migration, significantly reducing migration time while maintaining accuracy.
Solution Approach 2:
The patent creates a universal AIOps model that can operate across multiple cloud environments (AWS, Azure, Google Cloud, on-premise) without requiring separate models for each provider. The digital twin framework enables a single model to learn from diverse cloud configurations simultaneously, making the model multi-functional and capable of deploying to any cloud environment without retraining, thus improving productivity and reducing migration time.
3Measurement precision
If comprehensive cloud configuration data is collected for training, then model accuracy improves, but data collection complexity and computational overhead increase
Solution Approach 1:
The patent creates digital twins that replicate cloud configurations in a controlled virtual environment. Instead of collecting data from actual diverse cloud providers which requires complex data collection infrastructure, the system copies cloud configurations into digital twin representations. These virtual copies generate training data automatically, simplifying the data collection process while maintaining comprehensive coverage of different cloud architectures for accurate model training.
Solution Approach 2:
The digital twin framework acts as an intermediary between actual cloud configurations and the AIOps model training process. Rather than directly collecting data from multiple cloud providers (which would require complex data collection systems), the digital twins serve as intermediate virtual representations that generate training data. This intermediary layer simplifies data collection while preserving the diversity needed for accurate model training across different cloud environments.
Data Source
AI summary
A method, computer system, and a computer program product are provided. A first digital twin that models a first computing application being carried out in a first computing configuration is generated. The first digital twin replicates settings of the first computing configuration. A second digital twin is generated by altering the first digital twin. Respective time series data from the first digital twin, from the second digital twin, and from the first computing configuration are gathered. Drift in the gathered time series data is detected such that that different groups of data are produced. An artificial intelligence for information technology machine learning model (AIOPs model) is trained by implementing meta-learning domain generalization and by using training data divided according to the different groups of data.