CATO: End-to-End Optimization of ML-Based Traffic Analysis Pipelines
summary
CATO automatically searches hundreds of feature + packet-depth configurations, compiles full serving pipelines and measures them end to end, trading off latency and throughput against accuracy. It uses multi-objective Bayesian optimization to find Pareto-optimal points and relies on direct end-to-end measurement via a profiler that trains and deploys each candidate. Claims include up to 3600× lower inference latency, 3.7× higher zero-loss throughput, and ~15–17× faster convergence than random or simulated annealing.
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chapters (9)
- 0:00CATO framework
- 1:47Efficiency in traffic analysis
- 3:12Traffic representation trade-offs
- 5:40CATO end-to-end optimization
- 7:07Profiler’s direct measurement
- 8:11Bayesian optimization
- 9:13Pareto-optimal front
- 10:39Performance improvements
- 11:23Summary
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