[Paper Review] BARGAIN-MATCH: A Game Theoretical Approach for Resource Allocation and Task Offloading in Vehicular Edge Computing Networks

Zemin Sun, Geng Sun+3 more|arXiv (Cornell University)|Mar 26, 2022

Privacy-Preserving Technologies in Data4 citations

TL;DR

This paper proposes BARGAIN-MATCH, a game-theoretic framework for joint resource allocation and task offloading in vehicular edge computing (VEC) networks. It uses a bargaining game for intra-server resource allocation and many-to-one matching for inter-server load balancing, achieving stability, weak Pareto optimality, and polynomial-time complexity, with simulations showing superior system utility and efficiency under high workloads.

ABSTRACT

Vehicular edge computing (VEC) is emerging as a promising architecture of vehicular networks (VNs) by deploying the cloud computing resources at the edge of the VNs. This work aims to optimize resource allocation and task offloading in VEC networks. Specifically, we formulate a game theoretical resource allocation and task offloading problem (GTRATOP) that aims to maximize the system performance by jointly considering the incentive for cooperation, competition among vehicles, heterogeneity between VEC servers and vehicles, and inherent dynamic of VNs. Since the formulated GTRATOP is NP-hard, we propose an adaptive approach for resource allocation and task offloading in VEC networks by incorporating bargaining game and matching game, which is called BARGAIN-MATCH. First, for resource allocation, a bargaining game-based incentive is proposed to stimulate the vehicles and VEC servers to negotiate the optimal resource allocation and pricing decisions. Second, for task offloading, a many-to-one matching scheme is proposed to decide the optimal offloading strategies. Third, the dynamic and time-varying features are considered to adapt the strategies of BARGAIN-MATCH to the real-time VEC networks. Moreover, the BARGAIN-MATCH is proved to be stable and weak Pareto optimal. Simulation results demonstrate that the proposed BARGAIN-MATCH achieves superior system performance and efficiency compared to other methods, especially when the system workload is heavy.

Motivation & Objective

• To address the challenge of efficient resource management and task offloading in highly dynamic, computation-constrained vehicular edge computing (VEC) networks.
• To coordinate spatiotemporal heterogeneity in task demands and computational heterogeneity among VEC servers and cloud nodes.
• To jointly optimize intra- and inter-server resource allocation and task offloading strategies to maximize system utility.
• To design a low-complexity, stable, and Pareto-optimal algorithm suitable for real-time VEC environments.

Proposed method

• Proposes a hierarchical VEC architecture with SDN-based controller for coordinating horizontal (vehicle-vehicle, edge-edge) and vertical (vehicle-edge-cloud) collaboration.
• Formulates a joint resource allocation and task offloading problem (JRATOP) that integrates mobility, channel dynamics, NOMA, energy consumption, and task QoS requirements.
• Introduces a bargaining game model for intra-server resource allocation to incentivize fair and efficient pricing and allocation between vehicles and VEC/cloud servers.
• Employs a many-to-one matching framework for inter-server task offloading to balance loads across VEC servers and reduce congestion.
• Proves BARGAIN-MATCH is stable, weak Pareto optimal, and runs in polynomial time, ensuring practical deployability.
• Integrates non-orthogonal multiple access (NOMA) and mobility-aware task modeling to reflect real-world VEC dynamics.

Experimental results

Research questions

• RQ1 How can intra-server resource allocation be optimized to fairly and efficiently serve heterogeneous, delay-sensitive vehicular tasks under limited server capacity?
• RQ2 How can inter-server task offloading be designed to balance workloads across VEC servers and prevent congestion while minimizing end-to-end delay?
• RQ3 What game-theoretic mechanism can ensure stable and efficient collaboration between vehicles and edge/cloud servers in dynamic VEC environments?
• RQ4 How does the proposed approach perform under high system workloads compared to existing offloading and resource allocation schemes?

Key findings

• BARGAIN-MATCH achieves superior system utility, vehicle utility, and server utility compared to baseline methods, especially under heavy workloads.
• The algorithm reduces task processing delay and increases task processing rate significantly, demonstrating strong scalability in dense vehicular networks.
• Execution time increases linearly with the number of vehicles, confirming its polynomial-time complexity and practical feasibility.
• BARGAIN-MATCH outperforms OPOPRA in efficiency despite higher time complexity than direct decision-making schemes, and runs faster than EXO in dense networks.
• Performance improves by 19.28% to 52.02% when executed on higher-performance devices, indicating strong hardware scalability.
• The approach maintains stability and weak Pareto optimality, ensuring fair and efficient outcomes in all tested scenarios.