Smart Network Traffic Prediction for Scientific Applications
Whit Schonbein, Tinotenda Matsika, Ryan Eric Grant · 2024
Network traffic in HPC systems can impact application performance by inducing costly re-transmissions or consuming memory bandwidth. Emerging SmartNIC technologies present new opportunities for addressing these issues and optimizing network performance by providing a platform for the intelligent utilization of network resources through machine learning models of network traffic. However, SmartNICs also present challenges for deploying such models insofar as they offer relatively limited computational and memory resources, and these resources must be shared with other services. Based on an analysis of traffic data collected from eight scientific applications and proxies, we explore lightweight approaches to modeling network traffic using dynamic linear regression. Depending on the application, normalized root mean squared error for static regression may be less than 1 %, and dynamic regression can reduce this error by an order of magnitude. We further refine the dynamic approach by adding an additional classifier that categorizes predictions generated by the model as reliable or unreliable, showing that the technique can achieve good precision and recall. Finally, we evaluate the performance of dynamic regression and classification on NVIDIA BlueField-2 and BlueField-3 SmartNICs, demonstrating these computationally lightweight techniques are feasible on contemporary SmartNIC platforms.