Task Offloading in Vehicular Edge Computing Networks: A Load-Balancing Solution

• Published in: IEEE transactions on vehicular technology Vol. 69; no. 2; pp. 2092 - 2104
• Main Authors: Zhang, Jie, Guo, Hongzhi, Liu, Jiajia, Zhang, Yanning
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Ad hoc networks; Computation offloading; Computer architecture; Delay; Delays; Edge computing; FiWi; Information management; Load balancing; Load management; Mobile computing; mobile edge computing; SDN; Servers; Software-defined networking; Task analysis; Vehicles; vehicular edge computing networks; Wireless communication
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2019.2959410

Recently, the rapid advance of vehicular networks has led to the emergence of diverse delay-sensitive vehicular applications such as automatic driving, auto navigation. Note that existing resource-constrained vehicles cannot adequately meet these demands on low / ultra-low latency. By offloading parts of the vehicles' compute-intensive tasks to the edge servers in proximity, mobile edge computing is envisioned as a promising paradigm, giving rise to the vehicular edge computing networks (VECNs). However, most existing works on task offloading in VECNs did not take the load balancing of the computation resources at the edge servers into account. To address these issues and given the high dynamics of vehicular networks, we introduce fiber-wireless (FiWi) technology to enhance VECNs, due to its advantages on centralized network management and supporting multiple communication techniques. Aiming to minimize the processing delay of the vehicles' computation tasks, we propose a software-defined networking (SDN) based load-balancing task offloading scheme in FiWi enhanced VECNs, where SDN is introduced to provide supports for the centralized network and vehicle information management. Extensive analysis and numerical results corroborate that our proposed load-balancing scheme can achieve superior performance on processing delay reduction by utilizing the edge servers' computation resources more efficiently.