Loading…
Wednesday April 30, 2025 4:10pm - 4:30pm EDT
Gerry Wan, Stanford University; Shinan Liu, University of Chicago; Francesco Bronzino, ENS Lyon; Nick Feamster, University of Chicago; Zakir Durumeric, Stanford University


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 3600× lower inference latency and 3.7× higher zero-loss throughput while simultaneously achieving better model performance.


https://www.usenix.org/conference/nsdi25/presentation/wan-gerry
Wednesday April 30, 2025 4:10pm - 4:30pm EDT
Liberty Ballroom

Sign up or log in to save this to your schedule, view media, leave feedback and see who's attending!

Share Modal

Share this link via

Or copy link