Resumen
Cloud?fog computing is a large-scale service environment developed to deliver fast, scalable services to clients. The fog nodes of such environments are distributed in diverse places and operate independently by deciding on which data to process locally and which data to send remotely to the cloud for further analysis, in which a Service-Level Agreement (SLA) is employed to govern Quality of Service (QoS) requirements of the cloud provider to such nodes. The provider experiences varying incoming workloads that come from heterogeneous fog and Internet of Things (IoT) devices, each of which submits jobs that entail various service characteristics and QoS requirements. To execute fog workloads and meet their SLA obligations, the provider allocates appropriate resources and utilizes load scheduling strategies that effectively manage the executions of fog jobs on cloud resources. Failing to fulfill such demands causes extra network bottlenecks, service delays, and energy constraints that are difficult to maintain at run-time. This paper proposes a joint energy- and QoS-optimized performance framework that tolerates delay and energy risks on the cost performance of the cloud provider. The framework employs scheduling mechanisms that consider the SLA penalty and energy impacts of data communication, service, and waiting performance metrics on cost reduction. The findings prove the framework?s effectiveness in mitigating energy consumption due to QoS penalties and therefore reducing the gross scheduling cost.