The clouds are the most common medium for aggregating, storing and analyzing data from the Internet of Things (MIoT) based medical therapy applications used by patients or pharmaceutical knowledge in the conventional health care monitoring network. Mobile Web infrastructure, remote communication, and networking can cause data and other delays. In addition, a slight delay in response to the analyzed data could lead to incorrect decisions about care in case of an emergency that could jeopardize the life of the patient. Recently, an indirect coat of fog or edge projection has been utilized for the dispersed accessing and depositing data of MIoT to resolve networking and contact delays. Fuzzy Assisted Fog and Cloud Computing (FAFCC) with MIoT devices become the most favored traditional healthcare surveillance system approach. In this paper, it provides an application model for such a framework to illustrate how machine assets cost reduction is assured while efficiency limitations are guaranteed. Health access requests held in a fuzzy assisted fog-cloud delivery system. This model is established on a fuzzy system and can determine the minimum amount of computing assets needed to meet fog along with cloud projections agreement on Employment Level (SLA). The proposed model is approved and checked via distinct action simulator Java Modeling Tools (JMT). The results show that the proposed model can predict the response time of the system and accurately define the number of computing resources for health data services to achieve better performance.

云是用于汇总，存储和分析来自基于物联网（MIoT）的医学治疗应用程序的数据的最常用媒介，这些数据由患者或常规医疗保健监控网络中的药学知识使用。移动Web基础结构，远程通信和网络可能会导致数据和其他延迟。另外，对分析数据的响应略有延迟可能导致在紧急情况下做出错误的护理决定，从而危及患者的生命。最近，间接雾化或边缘投影已被用于分散访问和存储MIoT数据，以解决联网和联系延迟问题。带有MIoT设备的模糊辅助雾和云计算（FAFCC）成为最受欢迎的传统医疗监视系统方法。在本文中，它为这种框架提供了一个应用模型，以说明如何确保机器资产成本的降低，同时又保证了效率的限制。在模糊辅助雾云传输系统中保存的健康状况访问请求。该模型基于模糊系统建立，可以确定满足雾霾所需的最小计算资产数量以及就业水平（SLA）的云预测协议。通过不同的动作模拟器Java建模工具（JMT）批准并检查了提出的模型。结果表明，该模型可以预测系统的响应时间，并准确定义健康数据服务的计算资源数量，以实现更好的性能。