Exploring the application of Time Series Foundation Models to network monitoring tasks

Abstract

Modern network monitoring applications often rely on traditional machine learning models conceived for specific analysis tasks, which require extensive feature engineering, retraining for different use cases, and struggle with generalization. This lack of adaptability makes the deployment of AI/ML solutions in network monitoring a daunting task, as each new scenario requires significant reconfiguration, manual tuning, and retraining efforts, undermining the broader adoption of AI/ML for network traffic analysis.

Time Series Foundation Models (TSFMs), pre-trained on vast and diverse time-series datasets, offer a promising alternative in the network monitoring realm by enabling zero-shot and few-shot adaptability across different monitoring scenarios. In this work, we explore the potential of TSFMs for network monitoring by evaluating their performance in a challenging analysis task: estimating video streaming Quality of Experience (QoE) from encrypted network traffic. Our study assesses the zero-shot and few-shot capabilities of state-of-the-art TSFMs, the impact of time-series granularity, and the role of common traffic features in performance.

Using real-world video streaming QoE datasets, we show that TSFMs achieve competitive results in a zero-shot setting — plug-and-play approach, and that their performance can be easily and cost-effectively improved through few-shot learning techniques, even when applied on NetFlow-like features with coarse granularity. Beyond the specific video streaming QoE monitoring application, our findings demonstrate the viability and broader applicability of TSFMs to network monitoring tasks, opening the door to more scalable and generalizable network management solutions.