Multi-layer collaborative task offloading optimization: balancing competition and cooperation across local edge and cloud resources.

With the explosive growth of electronic information technology, mobile devices generate massive amounts of data and requirements, which poses a significant challenge to mobile devices with limited computing and battery capacity. Task offloading can transfer computing-intensive tasks from resource-constrained mobile devices to resource-rich servers, thereby significantly reducing the consumption of task execution. How to optimize the task offloading strategy in complex environments with multi-layers and multi-devices to improve efficiency becomes a challenge for the task offloading problem. We optimize the vertical assignment of tasks in a multi-layer system using deep reinforcement learning algorithms, which encompass the cloud, edge, and device layers. To balance the load among multiple devices, we employ the KNN algorithm. Subsequently, we introduce a task state discrimination method based on fuzzy control theory to enhance the performance of computing nodes under high load conditions. By optimizing task offloading policies and execution orders, we successfully reduce the average task execution time and energy consumption of mobile devices. We implemented the proposed algorithm in the PureEdgeSim simulator and performed simulations using different device densities to verify the algorithm's scalability. The simulation results show that the method we proposed outperforms the methods in previous work. Our method can significantly improve performance in high-device density scenarios. [ABSTRACT FROM AUTHOR]