Fog computing broadens the computing services to serve requests of Internet of Things (IoT) by resources at the edge of Cloud-Fog environments instead of serving these requests by resources at the environment’s core. The aim of fog computing is to reduce load of computing in data centers and reduce latency of requests, especially real-time ones. Load balancing and scheduling play essential roles and represent main key challenges to guarantee high throughput and reliability of services in Cloud-Fog environments. Therefore, this paper introduces a reliable scheduling approach for allocating customers’ requests to the resources of Cloud-Fog environments. The approach is called Load Balanced Service Scheduling Approach (LBSSA) and it considers load balancing among resources when assigning requests to them by classifying requests to real-time, important and time-tolerant. In addition, scheduling of requests in the proposed approach considers the failure rate of resources in order to provide high reliability for requested services. The approach has a set of algorithms for handling different types of requests. Simulation experiments using CloudSim are conducted to assess the LBSSA approach in terms of number of computing resources, utilization of resources, load balance variance and running time.

雾计算扩展了计算服务，可通过Cloud-Fog环境边缘的资源来满足物联网（IoT）的请求，而不是通过环境核心的资源来满足这些请求。雾计算的目的是减少数据中心的计算负荷并减少请求（尤其是实时请求）的延迟。负载平衡和调度扮演着至关重要的角色，并代表着主要的关键挑战，以确保Cloud-Fog环境中服务的高吞吐量和可靠性。因此，本文介绍了一种可靠的调度方法，用于将客户的请求分配给Cloud-Fog环境的资源。该方法称为负载均衡服务调度方法（LBSSA），它在通过将请求分类为实时资源时，在为资源分配请求时考虑资源之间的负载平衡，重要且耐时间的。另外，在所提出的方法中的请求调度考虑了资源的故障率，以便为所请求的服务提供高可靠性。该方法具有一组用于处理不同类型的请求的算法。进行了使用CloudSim的仿真实验，以评估LBSSA方法的计算资源数量，资源利用率，负载平衡差异和运行时间。