In the recent years, the Internet of Things (IoT) services has been increasingly applied to promote the quality of the human life and this trend is predicted to stretch for into future. With the recent advancements in IoT technology, fog computing is emerging as a distributed computing model to support IoT functionality. Since the IoT services will experience workload fluctuations over time, it is important to automatically provide the proper number of sufficient fog resources to address the workload changes of IoT services to avoid the over- or under-provisioning problems, meeting the QoS requirements at the same time. In this paper, an efficient resource provisioning approach is presented. This approach is inspired by autonomic computing model using Bayesian learning technique to make decisions about the increase and decrease in the dynamic scaling fog resources to accommodate the workload from IoT services in the fog computing environment. Also, we design an autonomous resource provisioning framework based on the generic fog environment three-tier architecture. Finally, we validate the effectiveness of our solution under three workload traces. The simulation results indicate that the proposed solution reduces the total cost and delay violation, and increases the fog node utilization compared with the other methods.

近年来，物联网（IoT）服务已被越来越多地用于提高人类生活质量，并且这一趋势预计将延续到未来。随着物联网技术的最新发展，雾计算正逐渐成为支持物联网功能的分布式计算模型。由于IoT服务将随着时间的推移而遇到工作负载波动，因此重要的是，自动提供适当数量的足够的雾资源来应对IoT服务的工作负载变化，以避免出现过多或不足的配置问题，同时满足QoS要求时间。在本文中，提出了一种有效的资源供应方法。这种方法的灵感来自使用贝叶斯学习技术的自主计算模型，以决定有关动态缩放雾资源的增加和减少，以适应雾计算环境中物联网服务的工作量。此外，我们设计了基于通用雾环境三层体系结构的自治资源供应框架。最后，我们在三个工作负载跟踪下验证了我们解决方案的有效性。仿真结果表明，与其他方法相比，该方案降低了总成本和延迟违规，提高了雾节点的利用率。最后，我们在三个工作负载跟踪下验证了我们解决方案的有效性。仿真结果表明，与其他方法相比，该方案降低了总成本和延迟违规，提高了雾节点的利用率。最后，我们在三个工作负载跟踪下验证了我们解决方案的有效性。仿真结果表明，与其他方法相比，该方案降低了总成本和延迟违规，提高了雾节点的利用率。