Generalized stochastic Petri net-based performance analysis of a Wi-Fi network probe in a dynamic QoX management system

Over the years, the concept of Quality of Service (QoS) has evolved from traditional network performance metrics to include Quality of Experience (QoE) considerations. This evolution also encompasses various business-related aspects, such as the impact of service quality on customer satisfaction, the alignment of service offerings with market demands, and the optimization of resource allocation to ensure cost-effectiveness and competitive advantage. This comprehensive approach, considering all the QoS dimensions (QoX), ensures the proper management of QoS across different services, contexts and technologies. Building on this broader QoX framework, it is essential to rely on advanced monitoring tools capable of handling the complexity introduced by these new demands. In this context, this paper describes a Generalized Stochastic Petri Net (GSPN) based model to analyze the performance of a Wi-Fi network probe in terms of computational capacity. The probe node plays a crucial role in a distributed monitoring system designed to implement a machine learning based global QoX management framework. Hence, the model explores the probe's computational resources to handle supplementary machine learning tasks alongside its typical packet capture and data processing responsibilities. Additionally, the model can evaluate the efficiency of the probe node under different scenarios, providing valuable insight into the potential need for additional resources at the node as operational demands continue to evolve.