The fog computing model has emerged as a viable infrastructure for serving IoT applications in recent years. In the fog ecosystem, it is essential to manage resources for different workloads due to the high volume and rapid growth of requests. Therefore, a challenge faced in this area is dynamic and efficient resource auto-scaling because fog resources must be allocated to requests efficiently. More fog resources than needed leads to “Over-Provisioning”, and fewer fog resources leads to the “Under-provisioning” issue. To this end, an effective deep learning-based resource auto-scaling mechanism has been proposed to manage the number of resources needed to handle dynamic workloads in a fog environment. The simulation results indicated that the proposed solution reduces cost, network usage, and delay violation and increases CPU utilization compared with existing resource auto-scaling mechanisms.

近年来，雾计算模型已成为服务物联网应用程序的可行基础设施。在雾生态系统中，由于请求量大且增长迅速，因此必须为不同的工作负载管理资源。因此，该领域面临的挑战是动态且高效的资源自动缩放，因为雾资源必须有效地分配给请求。雾资源多于需要导致“过度配置”，雾资源少导致“不足配置”问题。为此，已经提出了一种有效的基于深度学习的资源自动缩放机制来管理在雾环境中处理动态工作负载所需的资源数量。仿真结果表明，所提出的解决方案降低了成本、网络使用、