Cruise Control

Dynamic model selection for ML-based network traffic analysis

Cruise Control

Overview

Cruise Control is a dynamic model selection system that adaptively chooses the best ML model for network traffic analysis tasks based on real-time system conditions. Rather than selecting a single model offline, it maintains multiple pre-trained models with different accuracy-cost tradeoffs and dynamically switches between them to optimize performance under varying network conditions.

The Problem

Deploying machine learning models in real-world networks faces critical challenges:

  • Static Selection: Traditional approaches select a single “optimal” model offline based on accuracy
  • Changing Conditions: Network traffic load and system resources fluctuate constantly
  • Performance Constraints: Real-world deployments have strict latency and throughput requirements
  • Packet Loss: High traffic loads can overwhelm static model selections, leading to dropped packets
  • Resource Limitations: Single models can’t adapt to varying computational availability

What Cruise Control Does

Cruise Control takes an online, system-driven approach to model selection:

  1. Model Portfolio: Pre-trains multiple candidate models for the same task with different accuracy-cost tradeoffs
  2. Lightweight Monitoring: Uses lightweight signals representing current system traffic processing ability
  3. Dynamic Selection: Adaptively selects the most appropriate model based on real-time system state
  4. Continuous Adaptation: Switches models as network conditions change

Key Features

  • Multi-Model Architecture: Maintains portfolio of models with varying accuracy-efficiency tradeoffs
  • Real-Time Selection: Chooses models based on current system state, not offline metrics
  • Adaptive Response: Responds to fluctuating network loads and resource availability
  • Minimal Overhead: Uses lightweight signals for decision-making
  • Loss Prevention: Reduces packet drops during high traffic periods

Use Cases

  • Traffic classification under varying loads
  • Quality of Experience (QoE) inference in dynamic networks
  • Intrusion detection with resource constraints
  • Any network ML task with fluctuating traffic patterns
  • Real-time network analysis with performance SLAs

Results

  • 2.78% median accuracy improvement over offline-selected models
  • 4x reduction in packet loss compared to static model selection
  • Successfully tested on real-world traffic analysis tasks
  • Handles traffic classification, QoE inference, and intrusion detection

How It Works

Cruise Control monitors system-level indicators of traffic processing capacity and uses these signals to select from a pre-trained set of models. When traffic load is low, it can use higher-accuracy, computationally expensive models. Under high load, it switches to faster, more efficient models to prevent packet loss while maintaining acceptable accuracy.

Resources

Citation

@article{hugon2024cruise,
  title={Cruise Control: Dynamic Model Selection for ML-Based Network Traffic Analysis},
  author={Hugon, Johann and Schmitt, Paul and Busson, Anthony and Bronzino, Francesco},
  journal={arXiv preprint arXiv:2412.15146},
  year={2024}
}