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Asynchronous Task Scheduling and Resource Allocation for UAV-Enabled Mobile Edge Computing Networks
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AI
DOI：10.1109/TSC.2025.3645776.png)
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Abstract
En 中文
Mobile edge computing (MEC) is promising in handling delay-sensitive or resource-intensive tasks in mobile internet. Existing system schedulers in MEC networks usually schedule all service providers in a synchronous manner, which may not suit the practical scenario where tasks are randomly generated and require different computational resources and service times. In this work, we jointly optimize the task scheduling and resource allocation in an unmanned aerial vehicle (UAV)-enabled MEC network, where multiple UAVs asynchronously and cooperatively deliver task offloading and computing services to edge devices (EDs), for maximizing the average per-UAV energy utility and minimizing the overall task missing ratio. To enhance scheduling efficiency and jointly optimize task scheduling and resource allocation, we develop an asynchronous layered multi-agent proximal policy optimization (AL-MAPPO) algorithm, by incorporating the multi-UAV asynchronous action execution mechanism and a discrete-continuous layered action space into the general MAPPO framework. AL-MAPPO enables each UAV to perform flexible task scheduling and fine-grained resource allocation asynchronously. Extensive trace-driven simulations based on Alibaba Cluster Data V2017 validate the effectiveness of AL-MAPPO, compared with several baseline algorithms.
Keywords:
Mobile edge computing
asynchronous system operation
multi-agent reinforcement learning
task scheduling
resource allocation
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