An autonomous computation offloading strategy in Mobile Edge Computing: A deep learning-based hybrid approach

摘要

The fast growth of under developing internet-based technologies has been leading to propose promising methods to handle the heterogeneous massive volume of data produced by pervasive smart equipments such as handy mobile devices. Thanks to the mentioned technologies, these mobile devices can run critical business/entertainment applications such as Augmented Reality, Virtual Reality, vehicular networks, and media streaming. However, due to such devices' inherent limitations, some emerging computation environments such as Mobile Edge Computing have been introduced to achieve some essential requirements such as low latency, low energy consumption, and low cost. In the literature, offloading is a technique to transfer the burden of the mobile devices' work incurred by running applications' requests to these computation environments. On the other hand, exploring the computation environment to find the most efficient place to execute such requests is challenging work to achieve. In addition, different researches have been proposed to cope with the management problems of the offloading criterion. In this paper, an autonomous computation offloading framework is proposed to address some challenges related to time-intensive and resource-intensive applications. However, to the best of the authors' knowledge, the proposed autonomous framework has not been explored as a control model for self management in the computation offloading criterion. Besides, to cope with the large dimension of the offloading decision-making problem, different simulations including Deep Neural Networks, multiple linear regression, hybrid model, and Hidden Markov Model as the planning module of the mentioned autonomous methodology have been conducted. Simulation results show that the proposed hybrid model can appropriately fit the problem with near-optimal accuracy regarding the offloading decision-making, the latency, and the energy consumption predictions in the proposed self-management framework.