Abstract
Due to the non-stop and rapid spreading of virus pandemics all over the world, traditional healthcare monitoring capabilities of hospitals and/or medical centers are under a severe over-load. Modern computing infrastructures with the harmony of various layers of computing paradigms (e.g., cloud/fog/edge computing) for healthcare monitoring are apparently the essential computing backbone that help access and process instantly the medical data of every single patient at the very edge of the healthcare system to combat with global or regional virus contagion. Previous studies proposed different computing system architectures for healthcare monitoring but few works considered the evaluation of pure performance of medical data transmission in a comprehensive manner. In this paper, we proposed an M/M/c/K queuing network model for the performance evaluation of an Internet of Healthcare Things (IoHT) infrastructure in association with a three layer cloud/fog/edge computing continuum. The model considers a life cycle of medical data from body-attached IoT sensors in edge layer all the way to local clients (e.g., local medical doctors, physicians) through fog layer and to remote clients (e.g., medical professionals, patient’s family members) through cloud layer. Furthermore, we also explore the impact of the alteration in system configuration and computing capability of computing layers in two scenarios on various performance metrics. Critical performance metrics related to quality of service are evaluated in a comprehensive manner, such as (i) mean response time of medical data transmission to fog (local) clients and to cloud (remote) clients, (ii) utilization of cloud/fog/edge computing layers, (iii) service throughput, (iv) number of medical messages in a period of time, and (v) drop rate. The simulation results pinpoint bottle-neck parameters and configurations of the IoHT infrastructure’s system architecture in relation to the frequency of medical data collection for health check of patients. Thus, the findings of this study can help improve medical administration in hospitals and healthcare centers and help design computing infrastructures in accordance for medical monitoring in the severe circumstances of virus pandemics.
Highlights
The Internet of Things (IoT) is one of the great advances in modern technology
A queuing model is presented for performance evaluation, as a useful tool for designers of Internet of Healthcare Things (IoHT) scenarios based on Cloud-Fog-Edge resources to verify the performance of changes in the system even before they are implemented
It is a matter of curiosity that, (i) how the variation of periodic interval for medical data generated by health sensors attached on patients’ body and collected by edge devices in edge computing layer impacts the performance metrics of provided services and, (ii) how a specific configuration of each layer in the IoHT infrastructure and its variation impact the performance metrics of data transactions from patients’ attached health sensors all the way to internal clients at local medical centers through fog computing centers and to external clients at distant areas through cloud computing centers
Summary
The Internet of Things (IoT) is one of the great advances in modern technology. IoT devices are inter-communicable. In the case of IoHT for medical monitoring with the use of high-reliability equipment, a single variation of system architecture, operational configuration and/or parameters of devices/components apparently cause a sensitive impact on the performance of processing data and service delivery due to stream-like end-to-end data transactions from medical sensors surrounding patients at the edge of the network to display devices at the medical professionals’ working places. Pure performance analysis based on product form queuing network in these cases can consider different aspects in a quick manner such as resources capacity variation, sensor grouping by location, number of processing cores per machine. A queuing model is presented for performance evaluation, as a useful tool for designers of IoHT scenarios based on Cloud-Fog-Edge resources to verify the performance of changes in the system even before they are implemented.
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