Optimizing Mobile Edge Computing Multi-Level Task Offloading via Deep Reinforcement Learning

In a mobile edge computing (MEC) network, mobile devices could selectively offload tasks to the edge server(s) to save time and energy. However, we should consider many dynamic factors in task offloading optimization, which increases the complexity of this problem. Instead of executing the traditional optimization algorithm repeatedly, a well-trained empirical model such as an artificial neural network could be more efficient in decision making. In this research, considering the potential uneven spatial distribution of mobile devices in an MEC network with multiple wireless edge gateways, we allow an edge gateway to offload tasks to a nearby edge gateway further. We propose a deep reinforcement learning-based joint optimization approach for both device-level and edge-level task offloading. Experimental results show that the proposed approach achieves a near-optimal task delay performance and a better trade-off between the task delay and the energy consumption on tasks.