The transformation of smart mobility is unprecedented--Autonomous, shared and electric connected vehicles, along with the urgent need to meet ambitious net-zero targets by shifting to low-carbon transport modalities result in new traffic patterns and requirements for real-time computation at large-scale, for instance, augmented reality applications. The cloud computing paradigm can neither respond to such low-latency requirements nor adapt resource allocation to such dynamic spatio-temporal service requests. This paper addresses this grand challenge by introducing a novel decentralized optimization framework for mobility-aware edge-to-cloud resource allocation, service offloading, provisioning and load-balancing. In contrast to related work, this framework comes with superior efficiency and cost-effectiveness under evaluation in real-world traffic settings and mobility datasets. This breakthrough capability of 'computing follows vehicles' proves able to reduce utilization variance by more than 40 times, while preventing service deadline violations by 14%-34%.

翻译：智能移动出行正在经历前所未有的变革——自动驾驶、共享出行与电动联网车辆，以及通过转向低碳交通方式实现净零排放目标的迫切需求，催生了新型交通模式和大规模实时计算需求（例如增强现实应用）。云计算范式既无法满足此类低延迟需求，也无法针对动态时空服务请求调整资源分配。本文提出了一种新型去中心化优化框架，用于移动感知的边缘到云资源分配、服务卸载、资源调配与负载均衡，以应对这一重大挑战。与现有研究相比，该框架在实际交通场景与移动数据集评估中展现出卓越的效率和成本效益。这种"计算追随车辆"的突破性能力能够将资源利用率差异降低40倍以上，同时将服务截止时间违规率降低14%-34%。