Abstract
In a data-rich digital world, our hand-held resource-constrained mobile devices are restricted to performing small-to-medium-level computation processes and are incapable of performing high-computation processes. Computation offloading is a suitable solution for overcoming this shortcoming. Until recently, we have perceived cloud computing as an appropriate computation-offloading platform for mobile devices. However, cloud data centers, being far-end networks for mobile devices, increase the latency or network delay, which in turn affects the performance of real-time mobile Internet-of-Things applications. Hence, for critical real-time applications, a near-end network approach of computation offloading is required. Furthermore, the major hurdles for geographically distributed mobile devices are mobility and heterogeneity in the process of computation offloading. To overcome these challenges, the use of a deep-learning-based response-time-prediction framework is proposed in this paper to determine whether to offload in the nearby fog/edge node or neighbor fog/edge node, or cloud node. Furthermore, a restricted Boltzmann machines learning is applied to tackle the randomness in the availability of resources. We simulate the proposed model in MATLAB while considering the mobility and fluctuating resource demands of the end users. Implementing our deep-learning-based response-time-prediction framework improves the performance of the computation offloading because it facilitates a prompt selection of the offloading location.
•An efficient computation offloading model for edge/cloud is proposed.•A novel machine learning model is used for computation offloading.•Our method significantly improves the performance of the computation offloading.