Deep Q-learning enabled joint optimization of mobile edge computing multi-level task offloading

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. The traditional optimization approaches could require solving complex models to derive the optimal solution. This type of optimization problems are often NP-hard and will cause a considerable overhead on optimization. In contrast, 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 a 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 Q-learning-based joint optimization approach for both device-level and edge-level task offloading. We also design a centralized mathematical programming solution for exploring the optimal trade-off performance. Simulation results show that the proposed approach achieves a satisfactory task delay performance and a better trade-off between the task delay and the energy consumption on tasks.

Automated summary

This research addresses the issue of task offloading optimization in MEC networks by proposing a deep Q-learning-based joint optimization approach for both device-level and edge-level task offloading. Additionally, a centralized mathematical programming solution is designed to explore the optimal trade-off performance. Simulation results demonstrate satisfactory task delay performance and a better balance between task delay and energy consumption on tasks.