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Cloud computing has emerged as a popular computing milieu that provides a range of delivering solutions for small to large enterprises with a flexible model that allows a computing power and storing space for the large volumetric data within minimum cost. These days, computational paradigm is shifting towards utility-based pay-as-you-go model and many discussion aside, but there remains no canonical definition of cloud computing yet. In this paper we have proposed a service-oriented taxonomical spectrum of cloud computing, which is more focused on the service engineering perspective of cloud. Our argument behind cloud engineering is a layered structural approach 'as a Service' such as security as a service, fault tolerance as a service, architecture as a service. The main contribution of this paper is to identify a wide spectrum of taxonomy, aiming at a better understanding of functional as well as architectural components that could benefit from cloudification. We describe each sub-taxonomy (architecture, core services, security, fault tolerance, management services etc.) in details. In addition, we present a comparative study of several cloud systems based on taxonomy. Moreover, it also identifies many challenges and opportunities that exist on the landscape of enterprise cloud.

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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.