This paper presents an innovative framework that synergistically enhances computing performance through ubiquitous computing power distribution and dynamic computing node accessibility control via adaptive unmanned aerial vehicle (UAV) positioning, establishing UAV-enabled Computing Power Networks (UAV-CPNs). In UAV-CPNs, UAVs function as dynamic aerial relays, outsourcing tasks generated in the request zone to an expanded service zone, consisting of a diverse range of computing devices, from vehicles with onboard computational capabilities and edge servers to dedicated computing nodes. This approach has the potential to alleviate communication bottlenecks in traditional computing power networks and overcome the "island effect" observed in multi-access edge computing. However, how to quantify the network performance under the complex spatio-temporal dynamics of both communication and computing power is a significant challenge, which introduces intricacies beyond those found in conventional networks. To address this, in this paper, we introduce task completion probability as the primary performance metric for evaluating the ability of UAV-CPNs to complete ground users' tasks within specified end-to-end latency requirements. Utilizing theories from stochastic processes and stochastic geometry, we derive analytical expressions that facilitate the assessment of this metric. Our numerical results emphasize that striking a delicate balance between communication and computational capabilities is essential for enhancing the performance of UAV-CPNs. Moreover, our findings show significant performance gains from the widespread distribution of computing nodes.

翻译：本文提出一种创新框架，通过泛在计算能力分布与自适应无人机定位实现的动态计算节点可接入性控制协同增强计算性能，构建了无人机赋能的计算能力网络。在该网络中，无人机作为动态空中中继，将请求区域内生成的任务卸载至由多样化计算设备构成的服务区域，这些设备包括具备车载计算能力的车辆、边缘服务器以及专用计算节点。此方法有望缓解传统计算能力网络中的通信瓶颈，并克服多接入边缘计算中存在的"孤岛效应"。然而，如何量化通信与计算能力复杂时空动态性下的网络性能是一项重大挑战，其复杂性超越了传统网络。为此，本文引入任务完成概率作为核心性能指标，用于评估无人机赋能计算能力网络在规定端到端时延要求内完成地面用户任务的能力。运用随机过程与随机几何理论，我们推导出便于评估该指标的解析表达式。数值结果表明，通信与计算能力之间的精细平衡对于提升无人机赋能计算能力网络性能至关重要。此外，研究发现计算节点的广泛分布能带来显著的性能增益。