# CATO: End-to-End Optimization of ML-Based Traffic Analysis Pipelines

arXiv: https://arxiv.org/abs/2402.06099 · HTML: https://mlsanity.com/paper/2402.06099/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2024.

## Built on by (most cited first, 2 of 2)

- [Algorithmic Data Minimization for Machine Learning over Internet-of-Things Data Streams](https://mlsanity.com/paper/2503.05675/) (2025)
- [Versatile yet Efficient Network Traffic Analysis: Offloading Network Foundation Model to SmartNIC](https://mlsanity.com/paper/2508.02001/) (2025)

## Abstract (arXiv)

Machine learning has shown tremendous potential for improving the capabilities of network traffic analysis applications, often outperforming simpler rule-based heuristics. However, ML-based solutions remain difficult to deploy in practice. Many existing approaches only optimize the predictive performance of their models, overlooking the practical challenges of running them against network traffic in real time. This is especially problematic in the domain of traffic analysis, where the efficiency of the serving pipeline is a critical factor in determining the usability of a model. In this work, we introduce CATO, a framework that addresses this problem by jointly optimizing the predictive performance and the associated systems costs of the serving pipeline. CATO leverages recent advances in multi-objective Bayesian optimization to efficiently identify Pareto-optimal configurations, and automatically compiles end-to-end optimized serving pipelines that can be deployed in real networks. Our evaluations show that compared to popular feature optimization techniques, CATO can provide up to 3600x lower inference latency and 3.7x higher zero-loss throughput while simultaneously achieving better model performance.