# An Architecture for the Performance Management of Smart Healthcare Applications
## 2 Performance Management Approaches
### Network Slicing Approaches  
The emergent network slicing technology has been used in numerous studies for network performance management. Unlike static and exclusive policies, network slicing creates slices (sub-networks) by virtualization techniques over the same physical infrastructure and allows users to share virtual network resources. There are main types of slices, such as ultra-reliable and low-latency communications (URLLC) and enhanced mobile broadband (eMMB). S-health applications, such as remote surgery, require URLLC between wearables and infrastructure networks [14; 1]. However, network slicing impact on reliability and latency performance has been limited, and improving reliability methods has not been investigated on s-health [10]. Generally, studies on network slicing implement some network slice type, such as URLLC, following some priority queuing [17; 29; 30; 31], or present an end-to-end architecture [19; 37]. There are works combining network slicing with another performance management mechanism. For instance, [PERSON] [17] proposed a network slicing solution with priority queuing to provide ultra-reliability and low-latency in vehicular networks. The authors created three URLLC slices to control vehicle-state reports (e.g., speed, and location), event-drive (e.g., emergency), and user information. [PERSON] et al. [29] used network slicing with priority queuing to manage the different requirements, such as reliability, low latency, and high data rates. Considering a personalized medicine manufacturing use case, the authors created three levels: static telegram allocation (traffic from one application), shared telegram (traffic from several applications), and telegram overwriting (applications with higher priority overwrite network traffic). Both works improved reliability and latency. However, user density increased the latency, especially in vehicle context. Moreover, the studies only presented a mathematical model, not implementing any experiment with virtualization techniques. [PERSON] et al. [30] presented the challenges for mission-critical machine type communication and proposed to use orthogonal frequency-division multiplexing. Network simulations were presented, achieving 3 ms of latency for one link direction (not latency end-to-end). Although the author mentioned URLLC, they did not use network slicing and any virtualization technique. Virtual network resources are crucial to share, adapt, and allocate resources flexibly. [PERSON] et al. [31] presented a scheduling mechanism for URLLC and eMBB by exploiting the radio channel time-frequency variations. The authors have considered an urban 5G scenario. For load with 4-8 Mbps and 14 Mbps, the method reached 1.5 ms and 4 ms of latency, respectively. However, they have sliced the physical layer increasing considerable complexity. Virtualization techniques can be more adaptable and low-cost. [PERSON] et al. [18] and [PERSON] [32] presented a network slicing approach for critical communications on 5G and end-to-end delay. Based on network function virtualization, the authors proposed a queuing driven approach focusing on different applications. However, it was only considered a smart grid as a critical communication, abstaining from healthcare applications. Moreover, although virtualization techniques improve flexibility and automation, some works pointed out that, to efficiently manage network resources and meet performance requirements, network function virtualization techniques need mechanisms to predict performance degradation [41]. Packet classification also assists network functions to determine which action they should take for each incoming packet [42]. Therefore, improvements in virtualization techniques also are necessary to manage network resources efficiently. [PERSON] et al. [33; 34] proposed an admission control algorithm, and a scheduler to assign priority queue. The authors analyzed the network traffic per slice and defined six traffic classes, where streaming receives the highest priority level.