An Offloading Algorithm of Dense-Tasks for Mobile Edge Computing
Yunqing Xue, Xiao‐Jun Wu, Jing Yue · 2020
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. It is becoming more and more difficult to meet simultaneously the requirements of industrial applications in terms of latency and energy only using traditional cloud computing paradigm. Mobile edge computing (MEC), as a novel computing paradigm, promises dramatic reduction in latency, energy consumption and improve mobile service quality and enhance Quality of Experience by offloading computation-intensive tasks to edge servers in close proximity to mobile users. However, most researches of MEC offloading focused on one of two aspects: transmission delays and energy consumption. In this paper, different from these studies, three factors of energy consumption, latency and cloud computing costs are comprehensively considered. At the same time, two weight factors are introduced, which depend on the remaining power of MDs and the urgency of tasks to balance between execution delay, energy consumption and cloud computing cost. Then, the multiuser computation offloading problem is formulated as a constrained optimization problem, which is NP-hard. Due to the computation complexity of the formulated problem, An iterative heuristic task-intensive assignment algorithm is designed to make the offloading decision dynamically. Simulation results demonstrate that our algorithm outperforms the existing schemes in terms of offloading overhead.