# An Architecture for the Performance Management of Smart Healthcare Applications
## 2 Performance Management Approaches
Hence, the authors have employed ML-techniques to learn about network traffic to adjust some resources and enable various policy-driven network management. However, they have proposed only an abstraction-level architecture [26]. Although the authors have applied virtualization techniques to enable network management, they have considered only mobile applications from smartphones, not scalable for different scenarios [27]. Moreover, in general, the studies rely on network technology. For instance, for IoT, [PERSON] et al. [28] proposed a service-oriented scheduling model based on application and network layers classified into delay-sensitive and best-effort categories. Wi-Fi, by the Enhanced Distributed Channel Access (EDCA), modifies some parameters (e.g., control window) to give priority in the medium access. EDCA has four access categories, including voice and video [12]. However, nowadays, there is a more significant variety and diversity of types of applications, ranging from streaming or game to healthcare. Hence, such works do not support a wide range of applications. [PERSON] et al. [13] created a new access category, especially to medical alerts. The results achieved around 200 ms of latency for medical alerts. S-health applications with restricted requirements do not support such latency. Therefore, improvements are crucial to achieve specific requirements. In a cellular network context, there are studies concerned about critical/sensitive applications (e.g., alerts and mission critical). [PERSON] et al. [14] presented a 4G-5G architecture for s-health that classifies data vital into normal and abnormal by ML-techniques. Abnormal data receives a high priority. [PERSON] et al. [15] presented a 5G virtualization-based architecture for end-to-end reliability of mission-critical traffic in an urban scenario. Through virtualization techniques, the architecture gives priority for all mission-traffic over other types of traffic. Results showed a cost since the high data rate of critical sessions brings degradation to other users' services. [PERSON] et al. [16] proposed a scheduler algorithm and filtered more relevant healthcare data by different levels of priority. Although the authors have reduced the loss rate, the latency is still high. Moreover, these studies did not consider the management and customization of abnormal or critical data from different users. The architectures are not adaptable and easy-customized for different scenarios. In traffic separation, conventional technologies, such as virtual local area networks (VLAN), provide isolated networks for a specific application. However, in VLAN, it is difficult to control the availability and performance guarantee since the number of users operating in the network affects the entire system performance [6]. Therefore, the approaches mentioned above are still insufficient to accommodate the diversity of smart application requirements. Based on the literature, there is a diversity of network performance techniques due to unique network properties. Some applications cannot communicate their requirements, and the mapping between application requirements and network capabilities is not standard [38]. Hence, the appearance of some mechanism configurable between the applications and the network presents itself as a compelling alternative. Moreover, network virtualization can help achieve requirements since it controls virtual resources and supports the diversity of application, service, network control, and management [6]. The new paradigm, network slicing, is based on network virtualization. It has a considerable potential to achieve requirements over heterogeneous networks, especially for s-health applications that employ different network technologies in their communication [40]. Next, we present the most relevant works using network slicing in performance management context.