Performability Evaluation of Load Balancing and Fail-over Strategies for Medical Information Systems with Edge/Fog Computing Using Stochastic Reward Nets

Abstract

The aggressive waves of ongoing world-wide virus pandemics urge us to conduct further studies on the performability of local computing infrastructures at hospitals/medical centers to provide a high level of assurance and trustworthiness of medical services and treatment to patients, and to help diminish the burden and chaos of medical management and operations. Previous studies contributed tremendous progress on the dependability quantification of existing computing paradigms (e.g., cloud, grid computing) at remote data centers, while a few works investigated the performance of provided medical services under the constraints of operational availability of devices and systems at local medical centers. Therefore, it is critical to rapidly develop appropriate models to quantify the operational metrics of medical services provided and sustained by medical information systems (MIS) even before practical implementation. In this paper, we propose a comprehensive performability SRN model of an edge/fog based MIS for the performability quantification of medical data transaction and services in local hospitals or medical centers. The model elaborates different failure modes of fog nodes and their VMs under the implementation of fail-over mechanisms. Sophisticated behaviors and dependencies between the performance and availability of data transactions are elaborated in a comprehensive manner when adopting three main load-balancing techniques including: (i) probability-based, (ii) random-based and (iii) shortest queue-based approaches for medical data distribution from edge to fog layers along with/without fail-over mechanisms in the cases of component failures at two levels of fog nodes and fog virtual machines (VMs). Different performability metrics of interest are analyzed including (i) recover token rate, (ii) mean response time, (iii) drop probability, (iv) throughput, (v) queue utilization of network devices and fog nodes to assimilate the impact of load-balancing techniques and fail-over mechanisms. Discrete-event simulation results highlight the effectiveness of the combination of these for enhancing the performability of medical services provided by an MIS. Particularly, performability metrics of medical service continuity and quality are improved with fail-over mechanisms in the MIS while load balancing techniques help to enhance system performance metrics. The implementation of both load balancing techniques along with fail-over mechanisms provide better performability metrics compared to the separate cases. The harmony of the integrated strategies eventually provides the trustworthiness of medical services at a high level of performability. This study can help improve the design of MIS systems integrated with different load-balancing techniques and fail-over mechanisms to maintain continuous performance under the availability constraints of medical services with heavy computing workloads in local hospitals/medical centers, to combat with new waves of virus pandemics.

Model-Driven Impact Quantification of Energy Resource Redundancy and Server Rejuvenation on the Dependability of Medical Sensor Networks in Smart Hospitals

“…P represents the probability, and # represents the number of tokens in a given place. The downtime (D) can be obtained by Equation (1), where A represents the system availability and 8760 the number of hours in the year. The equation that calculates availability for all models is given by Equation ( 2), that is, the probability that the Power System and Smart Hospital are both working at the same time.…”

“…The contours of tubes in two-dimensional images usually appear as edges containing certain shallow features (such as gradient, intensity) and deep features (such as texture, shape, and spatial relation), as shown in Figure 1a. Accurate detection of these contours is very important for achieving three-dimensional reconstruction and measurement of tubes [1][2][3]. In theoretical research and practical applications, many scholars have proposed various methods that can be used for the detection of tube contours.…”

“…The Internet of Medical Things (IoMT) has become an important computing paradigm in recent years due to the emergence of new diseases around the world. In IoMT, wireless medical devices and sensors are integrated and harmonized into a common network in smart hospitals [1]. IoMT is a medical monitoring system that provides continuous realtime monitoring and observation services to patients through wearable health sensors and devices with wireless body area network (WBAN), artificial intelligence (AI), and remote monitoring techniques [2].…”

Supporting availability evaluation of a smart building monitoring system aided by fog computing

“…The evaluated scenario (shown in Figure 1) is composed of three essential parts that are also observed in previous studies [7, 9, 10], including: (i)a sensors computing layer, located at the building rooms, for continuous integration of temperature, motion and fire sensors; (ii)a communication network that encompasses all wireless telecommunications equipment, such as routers and repeaters distributed throughout the building. (iii)a fog computing layer for storage and monitoring to be used by the building administrator. In this work, we consider only one computing node characterized as a physical server called fog server node.…”

“…Unnecessary expenses can be made to obtain resources that will not be used. Analytical models (e.g., queuing nets, Petri nets, Markov chains) can be a solution in this context, allowing predictions based on probabilistic calculations in most cases [7][8][9][10][11][12][13].…”