With the increase in the number of Internet of Things (IoT) devices having limited resources, an extension of the cloud‐computing paradigm has emerged so‐called fog computing, where all the fog cells are located at the edge of the network and the latency can be reduced. Meanwhile, an important challenge has attracted much attention with the definition of fog computing is service placement problem that is still at its very beginning research. It allows to deployment IoT applications on computational fog resources, with the objective of optimizing quality of service (QoS) requirements of applications while taking into account maximizing the utilization of fog resources. In this paper, an autonomous IoT service placement methodology including four phases of monitoring, analysis, decision‐making, and execution is proposed called as (MADE). First, the available resources and application services' status are monitored at run time. Next, the requested services are prioritized with respect to application services' deadline. Then, the Strength Pareto Evolutionary Algorithm II is applied to take decisions about the application services placement as a multi‐objective optimization problem. Finally, the decisions made in the previous phases are executed in a fog environment. The experiment results indicate that the proposed methodology outperforms its counterparts in terms of different performance metrics.

中文翻译：

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雾计算中的自主物联网服务放置方法

随着资源有限的物联网（IoT）设备数量的增加，出现了云计算范式的扩展，即所谓的雾计算，其中所有雾单元均位于网络边缘，并且延迟可以减少。同时，雾计算的定义引起了一个重要的挑战，即服务放置问题仍处于起步阶段，而雾计算的定义仍处于起步阶段。它允许在计算雾资源上部署IoT应用程序，以优化应用程序的服务质量（QoS）要求，同时考虑最大程度地利用雾资源。本文提出了一种自主的物联网服务放置方法，该方法包括监视，分析，决策和执行四个阶段，称为（MADE）。第一的，在运行时监视可用资源和应用程序服务的状态。接下来，相对于应用程序服务的截止日期，对请求的服务进行优先级排序。然后，使用强度帕累托进化算法II来做出有关应用程序服务放置的决策，这是一个多目标优化问题。最后，在雾环境中执行前几个阶段中做出的决定。实验结果表明，在不同的性能指标方面，该方法优于同类方法。运用强度帕累托进化算法II来做出有关应用程序服务放置的决策，这是一个多目标优化问题。最后，在雾环境中执行前几个阶段中做出的决定。实验结果表明，在不同的性能指标方面，该方法优于同类方法。运用强度帕累托进化算法II来做出有关应用程序服务放置的决策，这是一个多目标优化问题。最后，在雾环境中执行前几个阶段中做出的决定。实验结果表明，在不同的性能指标方面，该方法优于同类方法。