# An Architecture for the Performance Management of Smart Healthcare Applications
## 2 Performance Management Approaches
### Network Slicing Approaches
In a second moment [34], the authors proposeda slice as a service to assist the slice optimization and orchestration. They decided on schedule or rescheduled slices based on the traffic analyzed. For instance, when there are mission-critical messages, other slices are rescheduled to support the critical requirements. However, the authors did not consider a real scenario since the streaming traffic did not has more priority traffic than other traffic (e.g., healthcare). Moreover, it is not clear the type of traffic rescheduled to support priority traffic. [PERSON] et al. [36] presented an allocation resource mechanism. The mechanism aims to slice the WiFi Access Point by considering the transmission time (airtime) as the resource. The authors identified the existing traffic flows, assigned a queue for each flow, and scheduled the traffic following the round-robin scheduling. Through network simulations, the mechanism allocated the airtime resource successfully. However, it was not presented performance metrics results (e.g., latency). Moreover, they did not consider the slice types (e.g., URLLC) and did not implement the traffic identification, only the scheduler. [PERSON] et al. [35] proposed a framework based on machine learning to identify the application requirements changes over time and achieve dynamic optimization. The authors considered multiple slices using different resources percentage from the total available resource pool. The results identified the traffic classes successfully, and the framework improved the throughput for eMBB traffic. However, the results are superficial since the authors only presented eMBB traffic results, abstaining from others, and the ML-techniques results only contain the accuracy, without network traffic details. In the context of network slicing with traffic shaping, [PERSON] et al. [43] tailored the network slices for each application, allowing flexible allocation of resources. Each application communicates its demands to the infrastructure and receives network resources. However, it is a crucial resource control mechanism since some applications can monopolize resources. [PERSON] et al. [37] and [PERSON] et al. [38] adopted a data-driven approach to quantify the efficiency of resource sharing. The authors adopted an overbooking policy, i.e., the operator allocates more resources than are actually available for the users, believing that some users will not use all resources allocated (they made an analogy with airplane and hotels companies). However, when some traffic does not receive resources, the packet data are considered best-effort traffic or is removed, increasing the packet losses. This approach is not suitable for the s-health scenario since critical applications do not support packet losses. Moreover, if applications do not follow agreements, some applications can request more resources than necessary, harming other applications. [PERSON] et al. [19] presented a mobile edge architecture for IoT to customize network resources. They proposed positioning network slicing technology in the gateway, closer to the users, to minimize latency. However, the authors presented only abstraction-level architecture. Based on the literature, there are several open issues related to network slicing since it is a novel concept. Several studies addressed the URLLC slice type, which involves critical applications with critical requirements, e.g., remote surgery and autonomous vehicles [17; 29; 31]. However, the studies addressed an individual characteristic of the network slicing concept, i.e., some studies addressed queuing priority. Other studies proposed a resource allocation method [44]. Nevertheless, network slicing can bring enormous benefits for s-health because it encompasses essential characteristics such as customization and adaptation. In this article, we propose an architecture for performance management in the s-health context based on network slicing.