Energy-Aware Task Offloading for Ultra-Dense Edge Computing

The rapid development of Internet of Things raises higher requirements on traffic volume and user connectivity density for 5G networks, and ultra-dense networks (UDN) are envisioned to be a developing trend to meet these demands. Meanwhile, with the increasing number of mobile devices (MDs) and computationally intensive applications, the conflict between the MDs limited computing and battery resources and the ever-increasing resource demands from the mobile applications becomes more and more prominent. To resolve these issues, mobile edge computing is expected to be a potential solution. Note that existing works on computation offloading in ultra-dense networks mostly focused on the problem of task offloading decision making and paid little attention to the MDs' status information, such as remaining battery and computation frequency. Toward this end, we provide this paper study the task offloading for ultra-dense edge computing, where the MDs' remaining battery and computation frequency are taken into account, and an energy-aware game theoretical offloading scheme is proposed as our solution. Numerical results show that our energy-aware task offloading scheme can not only save the MDs' energy efficiently but also achieve a delay-optimal task offloading decision profile for the tasks.