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

Gerry Wan, Shinan Liu, Francesco Bronzino, Nick Feamster, Zakir Durumeric

TL;DR

CATO tackles the practical gap in ML-based traffic analysis by jointly optimizing predictive performance and end-to-end serving costs. It introduces a multi-objective Bayesian optimization framework paired with a realistic profiler to automatically construct deployable pipelines that balance features, packet depth, and pipeline latency. The approach directly measures end-to-end cost and performance on real networks to identify Pareto-optimal configurations, and demonstrates substantial improvements in latency and zero-loss throughput while maintaining accuracy. This work enables practical, scalable deployment of ML-based traffic analysis in real networks and provides code for reproducibility.

Abstract

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.

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

TL;DR

CATO tackles the practical gap in ML-based traffic analysis by jointly optimizing predictive performance and end-to-end serving costs. It introduces a multi-objective Bayesian optimization framework paired with a realistic profiler to automatically construct deployable pipelines that balance features, packet depth, and pipeline latency. The approach directly measures end-to-end cost and performance on real networks to identify Pareto-optimal configurations, and demonstrates substantial improvements in latency and zero-loss throughput while maintaining accuracy. This work enables practical, scalable deployment of ML-based traffic analysis in real networks and provides code for reproducibility.

Abstract

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.
Paper Structure (10 sections, 3 figures, 1 table)

This paper contains 10 sections, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background and Motivation
  3. ML-Based Traffic Analysis
  4. Challenge of End-to-End Optimization
  5. Cost-Aware Traffic Analysis Optimization
  6. Problem Definition
  7. CATO Overview
  8. The CATO Optimizer
  9. The CATO Profiler
  10. Implementation of CATO

Figures (3)

  • Figure 1: A typical serving pipeline for ML-based traffic analysis. Usability of a model hinges on both its predictive accuracy and the systems performance of the entire pipeline.
  • Figure 2: Effects of different (feature set, packet depth) configurations on F1 score and execution time. We highlight the size and complexity of the search space.
  • Figure 3: CATO combines a multi-objective BO-based Optimizer and a realistic pipeline Profiler to construct and validate efficient ML-based traffic analysis serving pipelines.