Energy and Delay Co-Aware Intelligent Computation Offloading and Resource Allocation for Fog Computing Networks

In the data-rich everything-connected world, 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 intelligent fog computation offloading and resource allocation scheme is conceived in this paper. Specifically, we formulate a weighted sum minimization problem of the task completion time and energy consumption at the local fog to achieve efficient task computation. A deep learning-based joint offloading decision and resource allocation (DL-JODRA) algorithm is developed to address this problem by jointly optimizing the 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. Subsequently, to improve the efficiency and solution in a large-scale network scenario, a deep reinforcement learning-based joint offloading decision and resource allocation (DRL-JODRA) algorithm is constructed to obtain the optimal offloading and resource allocation policy. This approach is more suitable and stable for the solution of continuous-discrete action space by integrating the design of probabilistic discrete operation and inverting the gradient update. Finally, the extensive simulation results demonstrate that the proposed DL-JODRA and DRL-JODRA algorithms can rapidly achieve optimal offloading decision and resource allocation and gain a significant reduction in network costs (i.e., delay and energy) compared with other benchmark methods.