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：[translated abstract in Chinese] 雾计算通过将远程云环境的计算能力迁移至用户近端引入。这使得用户能够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，这解决了网络延迟和拥塞问题，其实现方式是在云与雾计算环境中部署服务。服务提供商需管理两种计算环境中的异构资源。近年来，资源管理策略已能实现从邻近雾节点和云向用户应用高效分配资源。然而，当服务提供商需从距最近雾节点多跳距离的远端雾节点为用户应用分配资源时，现有资源管理策略无法达到理想效果。此类资源管理问题的复杂性在多雾-云环境中急剧增加。这一难题促使我们重新审视并提出一种新型多雾-云环境下的启发式资源分配与优化算法——HeRAFC。该算法综合考虑用户应用优先级、执行时间与通信延迟，优化资源利用率并最小化云负载。通过对比相关算法进行性能评估，仿真结果表明所提HeRAFC算法相较其他算法具有更优效率。