To effectively process data across a fleet of dynamic and distributed vehicles, it is crucial to implement resource provisioning techniques that provide reliable, cost-effective, and real-time computing services. This article explores resource provisioning for computation-intensive tasks over mobile vehicular clouds (MVCs). We use undirected weighted graphs (UWGs) to model both the execution of tasks and communication patterns among vehicles in a MVC. We then study low-latency and reliable scheduling of UWG asks through a novel methodology named double-plan-promoted isomorphic subgraph search and optimization (DISCO). In DISCO, two complementary plans are envisioned to ensure effective task completion: Plan A and Plan B.Plan A analyzes the past data to create an optimal mapping ($\alpha$) between tasks and the MVC in advance to the practical task scheduling. Plan B serves as a dependable backup, designed to find a feasible mapping ($\beta$) in case $\alpha$ fails during task scheduling due to unpredictable nature of the network.We delve into into DISCO's procedure and key factors that contribute to its success. Additionally, we provide a case study that includes comprehensive comparisons to demonstrate DISCO's exceptional performance in regards to time efficiency and overhead. We further discuss a series of open directions for future research.

翻译：为了在动态分布的车辆编队中高效处理数据，必须实施能够提供可靠、经济且实时计算服务的资源供给技术。本文探讨了移动车载云中计算密集型任务的资源供给方案。我们采用无向加权图对移动车载云中的任务执行与车辆间通信模式进行建模，进而通过一种名为"双规划促进同构子图搜索与优化"（DISCO）的创新方法，研究无向加权图任务的低延迟与高可靠性调度问题。在DISCO框架中，我们设计了两个互补方案（方案A与方案B）以确保任务高效完成：方案A基于历史数据，在实际任务调度前预先构建任务与移动车载云间的最优映射(α)；方案B作为可靠备用方案，当网络不可预测性导致α在任务调度中失效时，可快速寻找可行映射(β)。我们深入剖析了DISCO的实施流程及其成功的关键要素，并通过包含全面对比的案例研究，展示了DISCO在时间效率与系统开销方面的卓越性能，最后探讨了一系列值得未来探索的开放研究方向。