The rapid advancement of 5G and 6G technologies has introduced various innovative applications, such as autonomous driving and augmented reality, which significantly increase network traffic and computational demands, particularly in densely populated areas. This study focuses on the utilization of unmanned aerial vehicles (UAVs) for Mobile Edge Computing (MEC) to address these challenges. Specifically, we explore the joint optimization of base station (BS) selection, computing resource allocation and UAV trajectory to minimize system delays and energy consumption in scenarios where computational requirements are related to user movement. A deep reinforcement learning-based approach, UM-DDPG, is proposed to optimize task offloading strategies and UAV trajectories. With our simulation platform, the results show that our method has been effective in reducing the system cost, including both delay and energy consumption, compared to traditional methods.

With the proliferation of sixth-generation (6G) communication networks, mobile edge computing (MEC) has become essential for addressing diverse user demands. The increasing heterogeneity of mobile devices, along with the rise of delay-sensitive and environmentally friendly tasks, exacerbates latency and energy consumption challenges. This paper proposes GERL-OA, a task offloading and resource allocation algorithm based on graph attention auto-encoder (GATE) assisted deep reinforcement learning (DRL). The MEC environment is modeled as an undirected graph, with spatial features extracted via graph neural network (GNN). GATE is utilized for unsupervised feature extraction, and a DRL framework is applied to optimize offloading decisions and resource allocation, minimizing the weighted sum of delay and energy consumption. Simulation results validate the superior convergence speed and global optimization capability of GERL-OA. Furthermore, extensive experimental results demonstrate the algorithm’s strong adaptability to different task types, confirming its effectiveness in balancing the requirements of delay-sensitive and environmentally friendly applications.

Probabilistic programming systems and machine learning frameworks like Pyro, PyMC3, TensorFlow, and PyTorch provide scalable and efficient primitives for inference and training. However, such operations are non-deterministic. Hence, it is challenging for developers to write tests for applications that depend on such frameworks, often resulting in flaky tests – tests which fail non-deterministically when run on the same version of code.