Fog computing has become an attractive research topic in recent years. As an extension of the cloud, fog computing provides computing resources for Internet of Things (IoT) applications through communicative fog nodes located at the network edge. Fog nodes assist cloud services in handling real-time and mobile applications by bringing the processing capability to where the data is generated. However, the introduction of fog nodes can increase scheduling openness and uncertainty. The scheduling issues in fog computing need to consider the geography, load balancing, and network latency between IoT devices, fog nodes, as well as the parent cloud. Besides, the scheduling methods also need to deal with the occurrence of uncertain events in real-time so as to ensure service reliability. This paper proposes an agent-based framework with a decentralized structure to construct the architecture of fog computing, while three agent-based algorithms are proposed to implement the scheduling, load balance, and rescheduling processes. The proposed framework is implemented by JADE and evaluated on the iFogSim toolkit. Experimental results show that the proposed scheduling framework can adaptively schedule tasks and resources for different service requests in fog computing and can also improve the task success rate when uncertain events occur.

翻译：雾计算近年来已成为一个引人关注的研究课题。作为云的扩展，雾计算通过位于网络边缘的通信雾节点为物联网应用提供计算资源。雾节点通过将处理能力延伸至数据生成处，辅助云服务处理实时及移动应用。然而，雾节点的引入可能增加调度的开放性与不确定性。雾计算中的调度问题需综合考虑物联网设备、雾节点及父云之间的地理位置、负载均衡和网络延迟。此外，调度方法还需实时应对不确定事件的发生，以确保服务可靠性。本文提出一种基于智能体的去中心化框架来构建雾计算架构，同时提出三种基于智能体的算法实现调度、负载均衡与重调度过程。所提框架通过JADE实现，并在iFogSim工具包上进行评估。实验结果表明，该调度框架能针对雾计算中不同服务请求自适应地调度任务与资源，并在不确定事件发生时提高任务成功率。