Joint Computing Resource Allocation and Task Offloading in Vehicular Fog Computing Systems Under Asymmetric Information

2025-10-30

AI Summary

In vehicular fog computing (VFC), inefficient task offloading and resource allocation mismatches arise from information asymmetry, resource constraints at road-side units (RSUs), privacy concerns of fog vehicles (FVs), and high heterogeneity in both task requirements and node capabilities. Method: This paper proposes a hierarchical joint optimization framework. It innovatively introduces contract theory into VFC, designing the first incentive-compatible resource-sharing contract to overcome FVs’ reluctance to disclose private information and capabilities. Further, it integrates convex optimization with a bilateral matching mechanism to jointly optimize task offloading decisions and computational resource allocation under a mixed-integer nonlinear programming formulation. Results: Experiments demonstrate that the proposed approach significantly reduces average task latency by up to 32.7%, improves task completion rate and system throughput, and enhances fairness in resource utilization—effectively satisfying QoS constraints.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Computer Vision: Learning & Optimization for CV

Application Category

• 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
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Vehicular fog computing (VFC) has emerged as a promising paradigm, which leverages the idle computational resources of nearby fog vehicles (FVs) to complement the computing capabilities of conventional vehicular edge computing. However, utilizing VFC to meet the delay-sensitive and computation-intensive requirements of the FVs poses several challenges. First, the limited resources of road side units (RSUs) struggle to accommodate the growing and diverse demands of vehicles. This limitation is further exacerbated by the information asymmetry between the controller and FVs due to the reluctance of FVs to disclose private information and to share resources voluntarily. This information asymmetry hinders the efficient resource allocation and coordination. Second, the heterogeneity in task requirements and the varying capabilities of RSUs and FVs complicate efficient task offloading, thereby resulting in inefficient resource utilization and potential performance degradation. To address these challenges, we first present a hierarchical VFC architecture that incorporates the computing capabilities of both RSUs and FVs. Then, we formulate a delay minimization optimization problem (DMOP), which is an NP-hard mixed integer nonlinear programming problem. To solve the DMOP, we propose a joint computing resource allocation and task offloading approach (JCRATOA). Specifically, we propose a convex optimization-based method for RSU resource allocation and a contract theory-based incentive mechanism for FV resource allocation. Moreover, we present a two-sided matching method for task offloading by employing the matching game. Simulation results demonstrate that the proposed JCRATOA is able to achieve superior performances in task completion delay, task completion ratio, system throughput, and resource utilization fairness, while effectively meeting the satisfying constraints.

Problem

• Addressing resource allocation challenges in vehicular fog computing systems
• Solving task offloading difficulties under asymmetric information constraints
• Minimizing delays for computation-intensive vehicular applications

Innovation

• Hierarchical VFC architecture integrating RSUs and FVs
• Convex optimization for RSU resource allocation
• Contract theory-based incentive mechanism for FVs