Seamless Graph Task Scheduling over Dynamic Vehicular Clouds: A Hybrid Methodology for Integrating Pilot and Instantaneous Decisions

2025-02-18

AI Summary

Real-time scheduling of graph-structured computational tasks in dynamic vehicular clouds faces challenges from volatile communication quality, fluctuating computing capacity, uncertain vehicle contact durations, and high data exchange overhead. Method: This paper proposes a low-latency, low-overhead subgraph–vehicle matching framework that jointly optimizes communication reliability, compute volatility, contact uncertainty, and data transfer costs. We introduce a hybrid scheduling paradigm integrating offline risk-aware prior subgraph search (RA-PilotISS) with online real-time isomorphic subgraph search (TE-InstaISS), synergistically balancing predictive capability and response robustness. The method leverages graph isomorphism matching and dynamic risk modeling within a unified vehicular cloud resource management architecture to enable coordinated online/offline decision-making. Results: Extensive experiments across multi-scale scenarios and diverse graph topologies demonstrate significant reductions in task completion time and subgraph search latency; average data exchange cost decreases by 23.7%, outperforming state-of-the-art approaches.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• Dynamic vehicular clouds resource allocation
• Minimizing task completion time
• Hybrid graph task scheduling methodology

Innovation

• Hybrid graph task scheduling methodology
• Risk-aware pilot isomorphic subgraph searching
• Time-efficient instantaneous isomorphic subgraph searching