SAM: Subseries Augmentation-Based Meta-Learning for Generalizing AIOps Model in Multi-Cloud Migration

Abstract

In the context of cloud computing, enterprises are increasingly adopting multi-cloud strategies to enhance performance, ensure cost efficiency, and avoid vendor lock-in. This trend presents a significant challenge for the migration of AI for IT operations (AIOps) models across different cloud providers due to variations in architecture, performance, and data distribution. Traditional methods of re-training AIOps models for new cloud environments are labor-intensive and delay deployment. To address this issue, we introduce a novel framework called SAM (Subseries Augmentation-based Meta-learning), which facilitates seamless model migration between clouds without the need for re-training from scratch. SAM leverages data augmentation and meta-learning to efficiently adapt AIOps models to new cloud environments. It has proven effective in adapting anomaly detectors across various config-urations over both public and simulated datasets. We believe that SAM can also be adapted to other AI models used for automating IT tasks such as alerting and resource scaling.