Abstract

The performance of vehicular edge computing (VEC) depends on the effective optimization of task offloading. However, uneven distribution of vehicular traffic, rapidly changing network conditions, and stochastic nature of vehicular networks motivate us to innovate approaches to efficient resource management while maintaining system's stability. to address these challenges, we propose a novel queue length-Based stochastic task migration strategy that leverages model predictive control (MPC) and Lyapunov optimization techniques. Our approach employs the queue length at the edge node as the criterion for offloading decisions. the MPC controller dynamically allocates the processing power and bandwidth resources to vehicles based on their current requirements, facilitating prompt offloading decisions. the Lyapunov optimization ensures long-term system stability. Our method also incorporates dynamic request selection from multi-dimensional queuing load optimization and ensures fair and efficient load distribution, thereby enhancing edge server utilization. We evaluate the performance of our proposed approach via simulation experiments and demonstrate its superiority by reducing the queue length at the edge node and adhering to delay constraints of vehicular networks.

Department(s)

Computer Science

Keywords and Phrases

edge offloading; Lyapunov optimization; model predictive control; Vehicular edge computing

International Standard Book Number (ISBN)

978-390317655-3

International Standard Serial Number (ISSN)

2690-3342; 2690-3334

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2025 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

01 Jan 2023

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