Cloud-fog computing for information-centric Internet-of-Things applications

This work develops a cloud-fog computing architecture for information-centric Internet-of-Things (IoT) applications with job classification and resource scheduling functions. The two designed functions support quality of service (QoS) by classifying IoT applications and scheduling computing resources. An innovative scheduling mechanism is developed to optimize the dispatch of cloud and fog resources at minimum cost in a cloud-fog computing environment. One cloud, three fogs, four types of IoT application and 2500 IoT tasks are designed in the cloud-fog simulation environment. The simulation results reveal that the proposed computing architecture outperforms computing without scheduling by reducing the cloud computing load by approximately 20.47%. The QoS of real-time IoT applications is guaranteed in the proposed mechanism.