**3. Use case**

Herein, we describe a scenario in which we can employ our proposed SDNMS-PAI for modeling the behavior of the network. We provide an example in the context of QoS service classes allocation, where the SDNMS-PAI is used to make smart choices in order to choose the best service classes on the E2E routing path to meet the E2E QoS requirements. Moreover, based on the Q-learning rewards more excellent service classes are selected in future. The traditional design of the internet mainly focusses on the reliability of services [16]. However, with 5G and beyond networks the requirements for applications have changed, and the applications demands for low latency with high data rates. Further, it is imperative whether the E2E QoS is according to the application service requests. Moreover, with heterogeneous networks on the path from source to destination, there exists several service classes in each domain. Hence, meeting the E2E QoS requirements for the applications service requests is a challenging problem.

Service class mapping mainly involves service classes allocation on the E2E path that meets the QoS demands of different service requests. The typical E2E service classes request for each application are different as shown in **Table 2**. For example, for application 1 the service requests are different than from application 2 and so on. Several solutions [40–42] have been proposed by researchers for service class mapping to meet the E2E QoS requirements for the applications. Furthermore, the mapping of the service classes is a challenging task with respect to meeting the E2E service needs due to the local view of the network state information in the domains.
