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Reliability-Aware Optimization of Task Offloading for UAV-Assisted Edge Computing
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AI
DOI：10.1109/TC.2025.3604463.png)
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Uncrewed aerial vehicles (UAV) are widely used for edge computing in poor infrastructure scenarios due to their deployment flexibility and mobility. In UAV-assisted edge computing systems, multiple UAVs can cooperate with the cloud to provide superior computing capability for diverse innovative services. However, many service-related computational tasks may fail due to the unreliability of UAVs and wireless transmission channels. Diverse solutions were proposed, but most of them employ time-driven strategies which introduce unwanted decision waiting delays. To address this problem, this paper focuses on a task-driven reliability-aware cooperative offloading problem in UAV-assisted edge-enhanced networks. The issue is formulated as an optimization problem which jointly optimizes UAV trajectories, offloading decisions, and transmission power, aiming to maximize the long-term average task success rate. Considering the discrete-continuous hybrid action space of the problem, a dependence-aware latent-space representation algorithm is proposed to represent discrete-continuous hybrid actions. Furthermore, we design a novel deep reinforcement learning scheme by combining the representation algorithm and a twin delayed deep deterministic policy gradient algorithm. We compared our proposed algorithm with four alternative solutions via simulations and a realistic Kubernetes testbed-based setup. The test results show how our scheme outperforms the other methods, ensuring significant improvements in terms of task success rate.
Keyword:
Optimization
Delays
Autonomous aerial vehicles
Reliability
Edge computing
Trajectory
Energy consumption
Cloud computing
Servers
Computational modeling
UAV-assisted network
multi-access edge computing (MEC)
deep reinforcement learning (DRL).
期刊
IF:
3.8
论文数:
5.4K
被引数:
9.8K
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