To effectively process high volume of data across a fleet of dynamic and distributed vehicles, it is crucial to implement resource provisioning techniques that can provide reliable, cost-effective, and timely computing services. This article explores computation-intensive task scheduling over mobile vehicular clouds (MVCs). We use undirected weighted graphs (UWGs) to model both the execution of tasks and communication patterns among vehicles in an MVC. We then study reliable and timely scheduling of UWG tasks through a novel mechanism, operating on two complementary decision-making stages: Plan A and Plan B. Plan A entails a proactive decision-making approach, leveraging historical statistical data for the preemptive creation of an optimal mapping ($\alpha$) between tasks and the MVC prior to practical task scheduling. In contrast, Plan B explores a real-time decision-making paradigm, functioning as a reliable contingency plan. It seeks a viable mapping ($\beta$) if $\alpha$ encounters failures during task scheduling due to the unpredictable nature of the network. Furthermore, we provide an in-depth exploration of the procedural intricacies and key contributing factors that underpin the success of our mechanism. Additionally, we present a case study showcasing the superior performance on time efficiency and computation overhead. We further discuss a series of open directions for future research.

翻译：为了高效处理动态分布式车队中的海量数据，实施资源调配技术以提供可靠、经济且及时的计算服务至关重要。本文探讨了移动车载云上的计算密集型任务调度问题。我们使用无向加权图对任务执行和车载云中车辆间的通信模式进行建模，并通过一种新颖机制研究无向加权图任务的可靠及时调度，该机制基于两个互补的决策阶段：方案A和方案B。方案A采用主动决策方法，利用历史统计数据在实际任务调度前预创建任务与车载云间的最优映射；相反，方案B探索实时决策范式，作为可靠的应急方案。当方案A的映射因网络不可预测性在任务调度中失效时，方案B寻求可行的映射。此外，我们深入剖析了该机制成功的过程细节和关键影响因素，并通过案例研究展示了其在时间效率和计算开销方面的优越性能，最后讨论了未来研究的若干开放方向。