OpenData: A Framework to Train and Deploy ML Solutions in Wide-Area Networks

Data-driven solutions hold significant promise for improving network protocols and services in wide area networks. However, their practical adoption in production networks has been limited. This paper investigates the potential of leveraging network data itself to enhance the effectiveness of data-driven solutions. We evaluate a Quality of Service (QoS) forecasting model trained on directly collected network data to demonstrate the advantages of harnessing networking data for machine learning purposes. Our results reveal that training the model with network data effectively addresses the challenges of Data Drift. We also acknowledge the limitations of designing a generic framework to support all problem domains. To overcome this challenge, we propose a comprehensive set of potential solutions that leverage network data for machine learning (ML) applications.