Rising aggressive virus pandemics urge to conduct studies on dependability and security of modern computing systems to secure autonomous and continuous operations of healthcare systems. In that regard, we propose to quantify dependability and security measures of an Internet-of-Medical Things (IoMT) infrastructure relied on an integrated physical architecture of cloud/fog/edge (CFE) computing paradigms in this article. We propose a reliability/availability quantification methodology for the IoMT infrastructure using a hierarchical model of three levels: 1) fault tree (FT) of overall IoMT infrastructure consisting of CFE member systems; 2) FT of subsystems within CFE member systems; and 3) continuous-time Markov chain (CTMC) models of components/devices in the subsystems. We incorporate a number of failure modes for the underlying subsystems, including Mandel-bug related failures and non-Mandel bugs related failure, as well as failures due to cyber-security attacks on software subsystems. Five case-studies of configuration alternation and four operational scenarios of the IoMT infrastructure are considered to comprehend the dependability characteristics of the IoMT physical infrastructure. The metrics of interest include reliability over time, steady state availability (SSA), sensitivity of SSA wrt. selected mean time to failure—equivalent (MTTFeq) and mean time to recovery—equivalent (MTTReq), and sensitivity of SSA wrt. frequencies of cyber-security attacks on software subsystems. The analysis results help comprehend operational behaviors and properties of a typical IoMT infrastructure. The findings of this study can improve the design and implementation of real-world IoMT infrastructures consisting of cloud, fog, and edge computing paradigms.

Modeling a complete Internet of Things (IoT) infrastructure is crucial to assess its availability and security characteristics. However, modern IoT infrastructures often consist of a complex and heterogeneous architecture and thus taking into account both architecture and operative details of the IoT infrastructure in a monolithic model is a challenge for system practitioners and developers. In that regard, we propose a hierarchical modeling framework for the availability and security quantification of IoT infrastructures in this paper. The modeling methodology is based on a hierarchical model of three levels including (i) reliability block diagram (RBD) at the top level to capture the overall architecture of the IoT infrastructure, (ii) fault tree (FT) at the middle level to elaborate system architectures of the member systems in the IoT infrastructure, and (iii) continuous time Markov chain (CTMC) at the bottom level to capture detailed operative states and transitions of the bottom subsystems in the IoT infrastructure. We consider a specific case-study of IoT smart factory infrastructure to demonstrate the feasibility of the modeling framework. The IoT smart factory infrastructure is composed of integrated cloud, fog, and edge computing paradigms. A complete hierarchical model of RBD, FT, and CTMC is developed. A variety of availability and security measures are computed and analyzed. The investigation of the case-study’s analysis results shows that more frequent failures in cloud cause more severe decreases of overall availability, while faster recovery of edge enhances the availability of the IoT smart factory infrastructure. On the other hand, the analysis results of the case-study also reveal that cloud servers’ virtual machine monitor (VMM) and virtual machine (VM), and fog server’s operating system (OS) are the most vulnerable components to cyber-security attack intensity. The proposed modeling and analysis framework coupled with further investigation on the analysis results in this study help develop and operate the IoT infrastructure in order to gain the highest values of availability and security measures and to provide development guidelines in decision-making processes in practice.

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 has been widely adopted over the years by practitioners and companies with a variety of requirements. With a strong economic appeal, cloud computing makes possible the idea of computing as a utility, in which computing resources can be consumed and paid for with the same convenience as electricity. One of the main characteristics of cloud as a service is elasticity supported by auto-scaling capabilities. The auto-scaling cloud mechanism allows adjusting resources to meet multiple demands dynamically. The elasticity service is best represented in critical web trading and transaction systems that must satisfy a certain service level agreement (SLA), such as maximum response time limits for different types of inbound requests. Nevertheless, existing cloud infrastructures maintained by different cloud enterprises often offer different cloud service costs for equivalent SLAs upon several factors. The factors might be contract types, VM types, auto-scaling configuration parameters, and incoming workload demand. Identifying a combination of parameters that results in SLA compliance directly in the system is often sophisticated, while the manual analysis is prone to errors due to the huge number of possibilities. This paper proposes the modeling of auto-scaling mechanisms in a typical cloud infrastructure using a stochastic Petri net (SPN) and the employment of a well-established adaptive search metaheuristic (GRASP) to discover critical trade-offs between performance and cost in cloud services.The proposed SPN models enable cloud designers to estimate the metrics of cloud services in accordance with each required SLA such as the best configuration, cost, system response time, and throughput.The auto-scaling SPN model was extensively validated with 95% confidence against a real test-bed scenario with 18.000 samples. A case-study of cloud services was used to investigate the viability of this method and to evaluate the adoptability of the proposed auto-scaling model in practice. On the other hand, the proposed optimization algorithm enables the identification of economic system configuration and parameterization to satisfy required SLA and budget constraints. The adoption of the metaheuristic GRASP approach and the modeling of auto-scaling mechanisms in this work can help search for the optimized-quality solution and operational management for cloud services in practice.

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.