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, calledtemplates, in a timely manner with minimized task completion time and data exchange overhead. To achieve this, wepropose ahybrid graphtaskscheduling (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.

Modern vehicular networks encounter a multitude of computation-intensive tasks that have unique processing topologies represented by graph structures. The integration of edge computing and vehicular networks has provided a unique platform for handling these tasks at the network edge. However, the complex structure of these tasks makes their scheduling and execution challenging. This paper proposes a Vehicular Computing-assisted Edge Network (VCEN) architecture, where graph tasks are scheduled over a Vehicle-Edge Collaborative Cloud (VECC) for parallel execution. Our goal is to obtain feasible mappings between task components and computing nodes in the VECC while minimizing task execution latency and energy consumption. We show that achieving this goal requires solving an NP-hard optimization problem with complex constraints related to task structure and VECC topology. We then propose a fast and lightweight approach for graph task scheduling over VECC that comprises two key phases. In the former phase, we introduce a preprocessing algorithm that reduces the graph task's dimensionality by merging important components and cutting redundant edges. In the latter phase, we deploy a cost-reduction-preferred mapping algorithm to obtain feasible mappings between task components and VECC. Through simulations, we demonstrate our superior performance in different network settings.