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

Zhang, Jie; Guo, Hongzhi; Li, Jiajia; Zhang, Yanning

doi:10.1109/tvt.2019.2959410

Cited by 273 publications

(106 citation statements)

References 38 publications

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“…Some works have used SDN as a strategy to improve offloading performance such as [153], [148], and [178]. Others have used clustering such as [46], [83], and [179].…”

Section: F: Networkmentioning

confidence: 99%

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Computation Offloading for Vehicular Environments: A Survey

et al. 2020

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With significant advances in communication and computing, modern day vehicles are becoming increasingly intelligent. This gives them the ability to contribute to safer roads and passenger comfort through network devices, cameras, sensors, and computational storage and processing capabilities. However, to run new and popular applications, and to enable vehicles operating autonomously requires massive computational resources. Computational resources available with the current day vehicles are not sufficient to process all these demands. In this situation, other vehicles, edge servers, and servers in remote data centers can help the vehicles by lending their computing resources. However, to take advantage of these computing resources, computation offloading techniques have to be leveraged to transfer tasks or entire applications to run on other devices. Such computation offloading can lead to improved performance and Quality of Service (QoS) for applications and for the network. However, computation offloading in a highly dynamic environment such as vehicular networks is a major challenge. Therefore, this survey aims to review and organize the computation offloading literature in vehicular environments. In addition, we demystify some concepts, propose a taxonomy with the most important aspects and classify most works in the area according to each category. We also present the main tools, scenarios, subjects, strategies, objectives, etc., used in the works. Finally, we present the main challenges and future directions to guide future research in this active research area.

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“…Some works have used SDN as a strategy to improve offloading performance such as [153], [148], and [178]. Others have used clustering such as [46], [83], and [179].…”

Section: F: Networkmentioning

confidence: 99%

“…Some works have also used more than one type of vehicular density in offloading experiments, such as: [135], [116], and [178].…”

Section: C: Highmentioning

confidence: 99%

Computation Offloading for Vehicular Environments: A Survey

et al. 2020

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“…However, most of works focusing on VECNs do not consider the load balancing of computing resources. Accordingly, Zhang et al in [13] strive to improve VECNs by a fiber-wireless technology. An algorithm is proposed to minimize the response latency when tasks are offloaded.…”

Section: A Computing Resource Related Optimizationmentioning

confidence: 99%

“…where inequation (12) ensures that the response latency of a i should not exceed the dwell time of vehicle i. The inequations (13) and (14) guarantee that the total computing and communication resources to be leased out cannot exceed the respective resources which R can provide (i.e., B and C).…”

Section: ) Objective Functionmentioning

confidence: 99%

“…Then, DbHA traverses h until it is empty. Each time a value is retrieved from h, the algorithm checks whether the corresponding application violates the constraints listed in inequations (12), (13) and (14). If all the constraint conditions hold, then R will accept this request, send confirmation message, allocate the corresponding resources, and update the related information such as the available resources and the total utility value (lines16-19).…”

Section: B Algorithm Designmentioning

confidence: 99%

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Intelligent Resource Allocation for Utility Optimization in RSU-Empowered Vehicular Network

et al. 2020

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Intelligent transportation system (ITS) has attracted extensive attention in both academia and industry for its potential benefits. For example, ITS is dedicated to convenient, economical and environmentally friendly service provisioning for the drivers and passengers in vehicles via advanced technologies including artificial intelligence (AI), knowledge mining, depth fusion, etc. Besides, several newly emerging computing paradigms revolved around ITS such as vehicular cloud and vehicular fog computing are proposed to fully exploit idle computing and communication resources within connected vehicles. As the number of vehicular applications is explosively increasing, it has posed great challenges to the limited capabilities of vehicle loaded computer systems and communication facility. Accordingly, more intelligent resource allocation strategies are needed for computationally intensive and time sensitive vehicular applications. In this paper we propose a road side unit (RSU) empowered vehicular network that consists of three hierarchical layers-vehicular cloud, RSU-enabled cloudlet, and central cloud, respectively. RSU is enhanced with edge servers such that it can intelligently respond to the resource requests in a real time fashion. To this end, an approximate but efficient resource allocation strategy is proposed that can intelligently optimize the utility value from the perspective of RSU-enabled cloudlet. Extensive experiments are carried out to evaluate the performance of the strategy. The results reveal that the proposed algorithm DbHA shows great advantages over other approaches such as the genetic algorithm (GA) and particle swarm optimization (PSO) in both respects (i.e., performance and response latency).

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Vehicular Networks

2022

Edge Networking

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

Cited by 273 publications

References 38 publications

Computation Offloading for Vehicular Environments: A Survey

Computation Offloading for Vehicular Environments: A Survey

Intelligent Resource Allocation for Utility Optimization in RSU-Empowered Vehicular Network

Vehicular Networks

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