 |
This paper proposed a deep learning-based partial offloading method in MEC systems for various scenarios. Two DNNs were employed for the selections of the partitioning of a single task and their offloading policy to solve the multiclass classification problems, respectively. For partitioning selection, the ratio of task size instead of the actual task size was considered as label data. The performance of the proposed method was evaluated in various scenarios. The simulation results showed that the proposed method has more than 77% and 89% classification performances for partitioning and offloading selection, respectively, in various scenarios. Applying appropriate weights depending on the application-specific requirements will reduce latency and energy. Our future work is to find a way to further increase accuracy by using a local search algorithm for the partitioning classification selection. |
IEEE/ICACT20220171 Slide.13
[Big Slide]
[YouTube]