Energy and Delay Co-aware Computation Offloading with Deep Learning in Fog Computing Networks

In data-rich everything connected world, the rapid and green data processing is essential, especially for some delay-sensitive and computation-intensive tasks. Motivated by these requirements, an energy and delay co-aware fog computation offloading mechanism is conceived in this paper. Specifically, we formulate a weighted sum minimization problem of task completion time and energy consumption at the local fog for achieving efficient task computation. Further, a deep learning-based joint offloading decision and resource allocation (DL-JODRA) algorithm is developed to address such problem by jointly optimizing offloading action, local CPU, bandwidth and external CPU occupation ratios. The optimal offloading decision based comprehensive optimization consideration of network resources further improves the network efficiency. Finally, the extensive simulation results demonstrate that the proposed DL-JODRA can achieve optimal offloading decision with low computation resource requirement and gain significant reduction on network costs (i.e., delay and energy) comparing with benchmark methods.