A bottom-up approach for QoS-driven network slicing in TSN networks

Network slicing enables multiple virtual networks to share physical resources, allowing network operators to deliver highly customizable and efficient networking solutions that meet the diverse requirements of modern applications. Automating the deployment and runtime management of network slices is essential for supporting network scaling with minimal human intervention. With several applications with different performance requirements, the manual configuration of slices is infeasible. Different initiatives have investigated methods in which network operators define high-level Key Performance Indicators that a network must deliver to networked applications, while the details of how to achieve these goals are abstracted by underlying control systems. Many works in the literature present a top-down approach, focusing on the high-level decision processes and relying on abstracted infrastructure managers and simulation tools to apply/execute such decisions. In this work, we leverage components that we previously developed for network monitoring, traffic shaping, and Software-Defined Time-Sensitive Networking to create a bottom-up approach toward automated slice management driven by Quality of Service goals. We describe the intricate coordination of elements required for an automated control loop and present the results achieved with a proof of concept executed in a real testbed of wired and Wi-Fi nodes. The applications use the data models introduced in this work to inform their requirements to the control plane. The control plane correctly prepares the network by applying rules for monitoring, traffic classification, and traffic shaping, effectively creating slices with different performance profiles for each application. Lastly, we present results using our control loop that continuously monitors each flow, identifies issues affecting the network performance, and takes corrective actions to reestablish the intended performance level of the applications.