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.

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.

Cloud computing emerges as a paradigm that facilitates on-demand access to technological resources through the mechanism of service virtualization. This virtualization enables the partitioning of hardware resources among applications that are organized into distinct independent modules. The concept of microservice architecture takes advantage of virtualization capabilities to embrace a software architecture strategy focused on the development of applications as assemblies of several interdependent but loosely coupled modules. Nonetheless, the adoption of microservices architecture is accompanied by substantial energy demands to meet the desired standards of performance and availability. Existing research within the domain of microservices has explored various topics pertinent to energy consumption, including elasticity, reliability, performance, and availability. Yet, the diversity of challenges and solutions presents a complex landscape for identifying prevailing research trends and unaddressed gaps in the context of microservices. This study aims to methodically discern, evaluate, and juxtapose the existing research trends and voids concerning energy consumption within microservices. It elucidates a systematic review on the subject of energy consumption in microservices architectures, offering a compilation of references to facilitate more directed future investigations. The initial selection encompassed 3625 articles, which were subsequently narrowed down through three stages of refinement, resulting in 37 articles chosen for an exhaustive review. These selected studies were cataloged and analyzed based on various criteria, including metrics, evaluation methodologies, and architectural typologies, thus uncovering research gaps and emerging trends related to energy consumption in microservice architectures. Furthermore, this inquiry delineates significant research challenges and prospective directions, structured around the key metrics that underpin the reviewed studies: performance, elasticity, scalability, reliability, sustainability, and availability.

The spread of the Coronavirus (COVID-19) pandemic across countries all over the world urges governments to revolutionize the traditional medical hospitals/centers to provide sustainable and trustworthy medical services to patients under the pressure of the huge overload on the computing systems of wireless sensor networks (WSNs) for medical monitoring as well as treatment services of medical professionals. Uncertain malfunctions in any part of the medical computing infrastructure, from its power system in a remote area to the local computing systems at a smart hospital, can cause critical failures in medical monitoring services, which could lead to a fatal loss of human life in the worst case. Therefore, early design in the medical computing infrastructure’s power and computing systems needs to carefully consider the dependability characteristics, including the reliability and availability of the WSNs in smart hospitals under an uncertain outage of any part of the energy resources or failures of computing servers, especially due to software aging. In that regard, we propose reliability and availability models adopting stochastic Petri net (SPN) to quantify the impact of energy resources and server rejuvenation on the dependability of medical sensor networks. Three different availability models (A, B, and C) are developed in accordance with various operational configurations of a smart hospital’s computing infrastructure to assimilate the impact of energy resource redundancy and server rejuvenation techniques for high availability. Moreover, a comprehensive sensitivity analysis is performed to investigate the components that impose the greatest impact on the system availability. The analysis results indicate different impacts of the considered configurations on the WSN’s operational availability in smart hospitals, particularly 99.40%, 99.53%, and 99.64% for the configurations A, B, and C, respectively. This result highlights the difference of 21 h of downtime per year when comparing the worst with the best case. This study can help leverage the early design of smart hospitals considering its wireless medical sensor networks’ dependability in quality of service to cope with overloading medical services in world-wide virus pandemics.

The domain of information security is undergoing significant evolution to address the increasingly complex challenges aimed at bolstering system resilience against attacks. The Moving Target Defense (MTD) methodology, which entails altering the system’s configuration—for instance, by relocating virtual machines (VM) or modifying IP addresses—serves to dynamically modify vulnerable components of a system. This strategy effectively disorients potential attackers, complicating their efforts to comprehend or anticipate the system’s configuration. Moreover, MTD can be proactively utilized by, for example, relocating VMs from a network segment that has been compromised. Consequently, MTD emerges as a viable approach for mitigating security concerns, particularly within cloud computing frameworks. A critical facet of MTD involves the system migration across different hardware, presenting logistical and strategic challenges that necessitate a thorough evaluation of factors such as operational downtime and the impact on system performance. Analytical models, particularly those based on stochastic Petri nets (SPN), offer a methodological advantage in strategizing MTD implementations by facilitating the assessment of potential outcomes in a non-live environment. This paper proposes an advanced model that extends prior research through the integration of an event-based MTD mechanism, which encompasses both the probability of intrusion detection and the ability to discern potential threats. Through the application of diverse migration initiation policies, this study aims to identify more efficacious strategies under specific conditions. The findings indicate that reliance on event-detection policies is advantageous when the system possesses a detection accuracy exceeding 50%, underscoring the critical role of precise intrusion detection in the efficacy of MTD strategies.