Unmanned aerial vehicles-assisted mobile edge computing (UMEC) can execute compute-intensive and latency-critical artificial intelligence (AI) services, which can be provided by multiple UAVs collaborating in the air to perform inference tasks. Completing an AI service requires multiple inferences, each of which is implemented by an AI service chain consisting of multiple virtual network functions (VNFs). The application of AISC relies on an efficient AISC deployment strategy to determine which UAV to deploy VNF on. However, the UMEC network topology is highly dynamic due to the high-speed movement of UAVs or their departure/arrival, which makes the AISC deployment in the UMEC network challenging. In addition, the intricate relationships between UMEC environment and AISC, as well as between individual VNFs in an AISC, can also affect the effectiveness of AISC deployment strategy. Moreover, under the constraints of energy consumption and load balancing, it is also difficult to optimize the AISC strategy to minimize AISC completion time for enhancing the quality of AI service. To address the above challenges, this paper proposes a double deep attention Q-network based on heterogeneous graph neural networks, which incorporates heterogeneous graph to capture diverse relationships in UMEC and utilizes attention mechanisms to adaptively focus on critical nodes and links for intelligent AISC deployment. The experimental results demonstrate that the proposed algorithm performs excellently in AISC completion time, AISC completion rate, load balancing and energy consumption.

翻译：无人机辅助移动边缘计算（UMEC）可执行计算密集且延迟敏感的AI服务，这些服务由多架无人机在空中协同执行推理任务。完成一项AI服务需要多次推理，每次推理由包含多个虚拟网络功能（VNF）的AI服务链（AISC）实现。AISC的应用依赖于高效的部署策略，以决定将VNF部署在哪些无人机上。然而，由于无人机高速移动或离开/到达导致的UMEC网络拓扑高度动态变化，使得UMEC网络中的AISC部署面临挑战。此外，UMEC环境与AISC之间、以及AISC内部各VNF之间的复杂关系也会影响AISC部署策略的有效性。在能耗和负载均衡约束下，如何优化AISC策略以最小化AISC完成时间从而提升AI服务质量也颇具难度。针对上述挑战，本文提出一种基于异构图神经网络的双深度注意力Q网络，该网络通过异构图捕捉UMEC中的多元关系，并利用注意力机制自适应关注关键节点和链路以实现智能AISC部署。实验结果表明，所提算法在AISC完成时间、AISC完成率、负载均衡和能耗方面均表现优异。