Seamless Graph Task Scheduling over Dynamic Vehicular Clouds: A Hybrid Methodology for Integrating Pilot and Instantaneous Decisions
Bingshuo Guo, Minghui Liwang, Xiaoyu Xia, Li Li, Zhenzhen Jiao, Seyyedali Hosseinalipour, Xianbin Wang
TL;DR
This paper tackles the challenge of scheduling graph-structured tasks over dynamic vehicular clouds by proposing a hybrid offline-online decision framework (P-HTS) that precomputes a robust template via RA-PilotISS and provides a rapid online backup via TE-InstaISS. The VC and task are modeled as undirected weighted graphs, with uncertainties in computing capacity, contact durations, and data exchange costs captured in probabilistic risk constraints. The offline component minimizes an expected cost combining task completion time and data exchange overhead, while the online component quickly adapts to current network conditions to obtain an optimal template when the offline one is invalid. Empirical results show that P-HTS achieves favorable trade-offs between scheduling time and performance metrics (CF, DEC, TCT) across varying VC scales and task topologies, highlighting its practicality for responsive edge computing in IoV contexts.
Abstract
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.