Operationalizing machine learning in real world networks

Applications of machine learning to networking, from performance diagnosis to security, have conventionally relied on models that are trained on offline packet traces, without regard to the limitations of existing measurement systems nor the cost of gathering, computing, and storing the corresponding input features. As a result, there remains a significant gap between the development of statistical models for network operations and their application and systemization in practice. In this talk, we explore the challenges of operationalizing machine learning models in real world networks. First, we develop new models to infer quality metrics (i.e., startup delay and resolution) for encrypted streaming video services and demonstrate the models are practical through a 16-month deployment in 66 homes. Building on the lessons learned, we design and develop Traffic Refinery, a new framework and system that enables a joint evaluation of both the conventional notions of machine learning performance (e.g., model accuracy) and the systems-level costs of different representations of network traffic. Traffic Refinery makes it possible to explore different representations for learning, balancing systems costs related to feature extraction and model training against model accuracy.

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