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

TL;DR

This paper tackles delay-sensitive task offloading in vehicular fog computing under asymmetric information by introducing a hierarchical VFC architecture that combines RSU-based MEC with idle FV resources coordinated by an MBS. It formulates a delay minimization problem (DMOP) that is NP-hard and proposes JCRATOA, which decomposes resource allocation into convex RSU allocation, contract-theory FV incentives, and a two-sided matching for offloading. The approach yields superior performance in task completion delay, completion ratio, system throughput, and resource utilization fairness, while respecting energy budgets, demonstrating scalability in dense urban settings. The work advances practical VFC deployment by addressing information asymmetry and heterogeneity, with a clear path to real-world validation and potential extension to UAV-assisted scenarios.

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.

Figures (5)

• Figure 1: The architecture of the hierarchical VFC system under asymmetric information consists of a vehicle layer, an edge layer, and a control layer. Each TV can execute the tasks locally, or offload the tasks to an RSU or an FV for edge computing. The RSUs, equipped with MEC servers, provide edge computing services and are connected to the MBS via fiber links. The MBS acts as a centralized controller, responsible for collecting system information and making offloading decisions under incomplete knowledge caused by the private information of FVs.
• Figure 2: The framework of JCRATOA. The original problem DMOP is first decomposed into a computing resource allocation subproblem and a task offloading subproblem. First, the computing resource allocation subproblem is further decomposed into subproblems of RSU computing resource allocation and FV computing resource allocation, which are solved by convex optimization and contract theory, respectively. Subsequently, the task offloading subproblem is solved through a two-sided matching method.
• Figure 3: System performance with respect to time.
• Figure 4: System performance with different number of TVs.
• Figure 5: System performance with different task sizes.

Theorems & Definitions (21)

• Remark 1
• Theorem 1
• Theorem 2
• Definition 1
• Definition 2
• Lemma 1
• Theorem 3
• Lemma 2
• Theorem 4
• Lemma 3