Energy efficient computing task offloading strategy for deep neural networks in mobile edge computing
Haoran Gao, Xiang Li, Bowen Zhou, Xiao Liu, Xu Jia · Figshare · 2020
Deep Neural Networks (DNNs) are widely used in mobile smart applications given their powerful data analysis capability. However, the complexity of their computing tasks brings a big challenge to end devices which are limited by their computing power and battery capacity. If the computing tasks in DNNs are offloaded completely to the cloud, the data transmission latency can be very significant. In contrast, with the advantage of low latency, distributed and location awareness, mobile edge computing can solve latency and energy-constrained problems in DNNs effectively. To optimize the energy-consumption of end devices with a user deadline constraint, the model of time and energy-consumption of computing tasks offloading in DNNs was established in the mobile edge computing. An energy efficient task offloading strategy for deep neural networks computing task in mobile edge computing was proposed. This strategy taken search layer of DNNs as a basic unit to divide computing task in DNNs and consider multiple computation resources synthetically in mobile edge computing environment during task offloading. A particle swarm optimization based task scheduling algorithm with multiple-resource task offloading was proposed, which could effectively optimize the energy-consumption of end devices under response time constraints. The experimental results showed that the proposed strategy could achieve the best fitness value compared with three other existing offloading strategies, and the end devices had the lowest energy consumption under response time constraints.