Improving IoT Services Using a Hybrid Fog-Cloud Offloading

Abstract

With the rapid development of the internet of things (IoT) devices and applications, the necessity to provide these devices with high processing capabilities appears to run the applications more quickly and smoothly. Though the manufacturing companies try to provide IoT devices with the best technologies, some drawbacks related to run some sophisticated applications like virtual reality and smart healthcare-based are still there. To overcome these drawbacks, a hybrid fog-cloud offloading (HFCO) is introduced, where the tasks associated with the complex applications are offloaded to the cloud servers to be executed and sent back the results to the corresponding applications. In the HFCO, when an IoT node generates a high-requirement processing task that cannot handle itself, it must decide to offload the task to the cloud server or to the nearby fog nodes. The decision depends on the conditions of the task requirements and the nearby fog nodes. Considering many fog nodes and many IoT nodes that need to offload their tasks, the problem is to select the best fog node to offload each task. In this paper, we propose a novel solution to the problem, where the IoT node has the choice to offload tasks to the best fog node or to the cloud based on the requirements of the applications and the conditions of the nearby fog nodes. In addition, fog nodes can offload tasks to each other or to the cloud to balance the load and improve the current conditions allowing the tasks to be executed more efficiently. The problem is formulated as a Markov Decision Process (MDP). Besides, a Q-learning-based algorithm is presented to solve the model and select the optimal offload policy. Numerical simulation results show that the proposed approach has superiority over other methods regarding reducing delay, executing more tasks, and balance the load.

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