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The problem of offloading policy is addressed for mobile edge computing (MEC) in this paper.
The proposal is a deep learning-based partial offloading technique to reduce user equipment's energy consumption and service delay. The proposed method consists of two deep neural networks (DNNs) to find the best partitioning of a single task and their offloading policy, respectively.
The DNN learned from the ratio of task size, not the actual task size, to improve the classification accuracy.
The performance of the proposed method was evaluated in three scenarios which are delay-critical model (DCM), energy-critical model (ECM), and delay and energy-critical model (DECM).
The simulation results show that the proposed method has more than 77% and 89% classification performances for partitioning and offloading in various scenarios.
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IEEE/ICACT20220171 Slide.02
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