By bringing computing capacity from a remote cloud environment closer to the user, fog computing is introduced. As a result, users can access the services from more nearby computing environments, resulting in better quality of service and lower latency on the network. From the service providers' point of view, this addresses the network latency and congestion issues. This is achieved by deploying the services in cloud and fog computing environments. The responsibility of service providers is to manage the heterogeneous resources available in both computing environments. In recent years, resource management strategies have made it possible to efficiently allocate resources from nearby fog and clouds to users' applications. Unfortunately, these existing resource management strategies fail to give the desired result when the service providers have the opportunity to allocate the resources to the users' application from fog nodes that are at a multi-hop distance from the nearby fog node. The complexity of this resource management problem drastically increases in a MultiFog-Cloud environment. This problem motivates us to revisit and present a novel Heuristic Resource Allocation and Optimization algorithm in a MultiFog-Cloud (HeRAFC) environment. Taking users' application priority, execution time, and communication latency into account, HeRAFC optimizes resource utilization and minimizes cloud load. The proposed algorithm is evaluated and compared with related algorithms. The simulation results show the efficiency of the proposed HeRAFC over other algorithms.

翻译：通过将计算能力从远程云环境迁移至用户近端，雾计算应运而生。由此，用户可从更邻近的计算环境获取服务，从而提升服务质量并降低网络延迟。从服务提供商的角度看，这解决了网络延迟与拥塞问题。该目标通过在云和雾计算环境中部署服务得以实现。服务提供商需管理这两种异构计算环境中的可用资源。近年来，资源管理策略已实现将邻近雾节点和云资源高效分配给用户应用。然而，当服务提供商需从距离最近雾节点多跳的雾节点为应用分配资源时，现有资源管理策略无法达到预期效果。此类资源管理问题的复杂度在多雾-云环境中急剧增加。这促使我们重新审视并提出一种新颖的多雾-云环境启发式资源分配与优化算法（HeRAFC）。HeRAFC综合考虑用户应用优先级、执行时间及通信延迟，优化资源利用率并最小化云负载。该算法经过评估并与相关算法对比，仿真结果证明了HeRAFC相较于其他算法的有效性。