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 to demonstrate DISCO's commendable performance in regards to time efficiency and overhead. We further discuss a series of open directions for future research.

翻译：摘要：为了在动态分布的车辆编队中高效处理数据，必须实施能提供可靠、经济且实时计算服务的资源供给技术。本文针对移动车载云（MVC）中计算密集型任务的资源供给问题展开研究。我们采用无向加权图（UWG）对任务执行过程及MVC中车辆间的通信模式进行建模，进而通过一种名为"双规划促进同构子图搜索与优化"（DISCO）的新方法，研究UWG任务的低延迟与高可靠调度。在DISCO中，我们设计了两个互补方案（方案A和方案B）以确保任务有效完成：方案A在实际任务调度前通过分析历史数据，预先建立任务与MVC之间的最优映射（α）；方案B作为可靠后备方案，当因网络不可预测性导致α在任务调度中失效时，能够快速寻找到可行映射（β）。本文深入剖析了DISCO的实现流程及其成功的关键因素，并通过案例研究展示了该方法在时间效率与开销方面的优异性能。最后，我们讨论了未来研究的若干开放方向。