Network Assurance in Intent Based Data Center Networking: A Domain Shift Approach

Intent-based networking (IBN) is driven by network assurance principles, aiming to ensure performance reliability through continuous monitoring and automated adjustments. As such, in a data center context, Virtual Machine (VM) resource utilization metrics are critical for predicting network behavior and enhancing network assurance. Yet, each VM presents unique resource usage patterns, which stem from varying statistical properties due to application characteristics, underlying hardware, and user behavior. These variations could lead to a phenomenon known as domain shift in the Machine Learning (ML) field. In this paper, we first introduce the challenge of domain shift in VM resource utilization prediction. We then evaluate the performance robustness of state-of-the-art ML models for VM resource utilization prediction, such as various Recurrent Neural Networks (RNNs), Transformers, Informers, as well as lightweight zero-shot and few-shot approaches. Extensive experimentation in a real-world dataset indicates that the Gated Recurrent Units (GRU) model generalizes more effectively while maintaining a low computational footprint.