Model Placement for Quality Inference of Video Streaming Traffic over a Cellular Network

Monitoring the quality of streaming video applications is important for Internet service providers (ISPs) to detect network issues and facilitate capacity planning. Machine Learning (ML) inference models have emerged as an effective solution to determine service quality using network traffic. However, while much focus has been on enhancing model performance, little attention has been given to deploying these models across entire networks. This paper introduces a new placement approach of quality inference models and their associated tasks to enhance the monitoring of video streaming applications over an entire mobile traffic network. Starting from the observation that inference tasks require the deployment of multiple components to, first, calculate input features from raw traffic, and then execute the inference models, we define the placement problem as an integer programming problem and, given its NP-hardness, we provide a heuristic solution, experimentally close to the optimum, based on the relaxation and the rounding of fractional solutions. We highlight that decoupling these components for the inference of network traffic can be beneficial in terms of total accuracy of the ML inference tasks. Finally, we experimentally show that our solution outperforms state-of-the-art placement techniques by ~30% of accuracy of the deployed inference models.