In healthcare applications, numerous sensors and devices produce massive amounts of data which are the focus of critical tasks. Their management at the edge of the network can be done by Fog computing implementation. However, Fog Nodes suffer from lake of resources That could limit the time needed for final outcome/analytics. Fog Nodes could perform just a small number of tasks. A difficult decision concerns which tasks will perform locally by Fog Nodes. Each node should select such tasks carefully based on the current contextual information, for example, tasks' priority, resource load, and resource availability. We suggest in this paper a Multi-Agent Fog Computing model for healthcare critical tasks management. The main role of the multi-agent system is mapping between three decision tables to optimize scheduling the critical tasks by assigning tasks with their priority, load in the network, and network resource availability. The first step is to decide whether a critical task can be processed locally; otherwise, the second step involves the sophisticated selection of the most suitable neighbor Fog Node to allocate it. If no Fog Node is capable of processing the task throughout the network, it is then sent to the Cloud facing the highest latency. We test the proposed scheme thoroughly, demonstrating its applicability and optimality at the edge of the network using iFogSim simulator and UTeM clinic data.

中文翻译：

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MAFC：用于医疗保健关键任务管理的多代理雾计算模型。

在医疗保健应用中，大量传感器和设备产生大量数据，这是关键任务的重点。它们在网络边缘的管理可以通过Fog计算实现来完成。但是，雾节点饱受资源之苦，这可能会限制最终结果/分析所需的时间。雾节点只能执行少量任务。一个困难的决定涉及雾节点将在本地执行哪些任务。每个节点应基于当前上下文信息（例如，任务的优先级，资源负载和资源可用性）仔细选择此类任务。我们在本文中建议用于医疗关键任务管理的多智能体雾计算模型。多代理系统的主要作用是在三个决策表之间进行映射，以通过为任务分配优先级，网络负载和网络资源可用性来优化调度关键任务。第一步是确定是否可以在本地处理关键任务。否则，第二步涉及对最合适的邻居Fog Node进行复杂的选择以对其进行分配。如果没有Fog Node能够处理整个网络中的任务，则将其发送到面临最高延迟的Cloud。我们使用iFogSim模拟器和UTeM临床数据对所提出的方案进行了全面的测试，以证明其在网络边缘的适用性和最佳性。第一步是确定是否可以在本地处理关键任务。否则，第二步涉及对最合适的邻居Fog Node进行复杂的选择以对其进行分配。如果没有Fog Node能够处理整个网络中的任务，则将其发送到面临最高延迟的Cloud。我们使用iFogSim模拟器和UTeM临床数据对所提出的方案进行了全面的测试，以证明其在网络边缘的适用性和最佳性。第一步是确定是否可以在本地处理关键任务。否则，第二步涉及对最合适的邻居Fog Node进行复杂的选择以对其进行分配。如果没有Fog Node能够处理整个网络中的任务，则将其发送到面临最高延迟的Cloud。我们使用iFogSim模拟器和UTeM临床数据对所提出的方案进行了全面的测试，以证明其在网络边缘的适用性和最佳性。