Vehicular clouds (VCs) play a crucial role in the Internet-of-Vehicles (IoV) ecosystem by securing essential computing resources for a wide range of tasks. This paPertackles the intricacies of resource provisioning in dynamic VCs for computation-intensive tasks, represented by undirected graphs for parallel processing over multiple vehicles. We model the dynamics of VCs by considering multiple factors, including varying communication quality among vehicles, fluctuating computing capabilities of vehicles, uncertain contact duration among vehicles, and dynamic data exchange costs between vehicles. Our primary goal is to obtain feasible assignments between task components and nearby vehicles, called templates, in a timely manner with minimized task completion time and data exchange overhead. To achieve this, we propose a hybrid graph task scheduling (P-HTS) methodology that combines offline and online decision-making modes. For the offline mode, we introduce an approach called risk-aware pilot isomorphic subgraph searching (RA-PilotISS), which predicts feasible solutions for task scheduling in advance based on historical information. Then, for the online mode, we propose time-efficient instantaneous isomorphic subgraph searching (TE-InstaISS), serving as a backup approach for quickly identifying new optimal scheduling template when the one identified by RA-PilotISS becomes invalid due to changing conditions. Through comprehensive experiments, we demonstrate the superiority of our proposed hybrid mechanism compared to state-of-the-art methods in terms of various evaluative metrics, e.g., time efficiency such as the delay caused by seeking for possible templates and task completion time, as well as cost function, upon considering different VC scales and graph task topologies.

翻译：车载云（VCs）通过为各类任务提供关键计算资源，在车联网（IoV）生态系统中发挥着至关重要的作用。本文致力于解决动态车载云中计算密集型任务的资源供给难题，此类任务以无向图表示，可在多车辆上进行并行处理。我们通过考虑多种因素对车载云的动态性进行建模，包括车辆间变化的通信质量、车辆波动的计算能力、车辆间不确定的接触时长以及车辆间动态的数据交换成本。我们的主要目标是及时获得任务组件与附近车辆（称为模板）之间的可行分配方案，以最小化任务完成时间和数据交换开销。为此，我们提出了一种混合图任务调度（P-HTS）方法，该方法结合了离线和在线两种决策模式。针对离线模式，我们引入了一种称为风险感知先导同构子图搜索（RA-PilotISS）的方法，该方法基于历史信息预先预测任务调度的可行解。随后，针对在线模式，我们提出了时间高效的瞬时同构子图搜索（TE-InstaISS），作为备用方案，用于在RA-PilotISS识别的调度模板因条件变化而失效时，快速识别新的最优调度模板。通过全面的实验，我们证明了在考虑不同车载云规模和图任务拓扑结构的情况下，所提出的混合机制在多种评估指标（例如，寻求可能模板导致的延迟和任务完成时间等时间效率指标，以及成本函数）上均优于现有先进方法。