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To reduce the number of classes for partitioning and offloading policy selections, we first used two DNNs for partitioning and offloading policy selections, respectively. It could significantly reduce the combination of classes between partitioning and offloading policy. Second, the class for partitioning selection is generated through a ratio of size rather than actual size. It mitigates the rapid increase in the number of classes as data size increases. The proposed method uses two techniques to perform classification tasks through significantly reduced classes, enabling low complexity and reliable selections for partitioning and offloading policy.
As I mentioned earlier, Performance comparison and analysis are performed through various models in environments where service delay is the dominant factor, energy consumption is the dominant factor, and service delay and energy consumption are considered the dominant factor.
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IEEE/ICACT20220171 Slide.05
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