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Various studies have shown the benefits of using distributed fog computing for healthcare systems. The new pattern of fog and edge computing reduces latency for data processing compared to cloud computing. Nevertheless, the proposed fog models still have many limitations in improving system performance and patients' response time.This paper, proposes a new performance model by integrating fog computing, priority queues and certainty theory into the Edge computing devices and validating it by analyzing heart disease patients' conditions in clinical decision support systems (CDSS). In this model, a Certainty Factor (CF) value is assigned to each symptom of heart disease. When one or more symptoms show an abnormal value, the patient's condition will be evaluated using CF values in the fog layer. In the fog layer, requests are categorized in different priority queues before arriving into the system. The results demonstrate that network usage, latency, and response time of patients' requests are respectively improved by 25.55%, 42.92%, and 34.28% compared to the cloud model. Prioritizing patient requests with respect to CF values in the CDSS provides higher system Quality of Service (QoS) and patients' response time.

Virtualization as one of the leading technologies has assisted datacenters to cloudify their products and provide versatile platforms and variety of Internet services. This technology also has facilitated agile deployment of complex Internet services such as Cloud-based multi-tier applications. However implementing multi-tier applications assists providers to set up flexible and scalable services, deploying such applications in virtualized environments faces challenges, which harden collocation of disparate multi-tier applications in a shared environment. In this article, we propose a placement strategy to consolidate multi-tier applications' tiers in a virtualized datacenter regarding workload demands of individual tiers, utilization of physical hosts' resources and operational status of virtual machines. The placement strategy identifies over-utilized hosts based on the birth--death stochastic process and ranks tiers based on Data Envelopment Analysis-Analytic Hierarchy Process modeling to be placed on target hosts. A prioritized tier will be placed on an appropriate host though least operational interference with hosted tiers incurred. The efficiency of the proposed model is evaluated using a series of in-depth experiments. We introduce results derived from quantitative and qualitative analyses that are useful for multi-tier applications placement in virtualized datacenters. Simulation results reveal that the proposed solution excels in terms of both load distribution and energy consumption in the datacenter, while the number of unnecessary migrations and consecutive Service Level Agreement violations is considerably reduced. [ABSTRACT FROM AUTHOR]