Joint communication and computing resource allocation in vehicular edge computing

Sun, Jianan; Gu, Qing; Zheng, Tao; Dong, Ping; Qin, Yajuan

doi:10.1177/1550147719837859

Cited by 40 publications

(26 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this work a multi-objective VEC task scheduling algorithm is proposed for task offloading from user vehicles to MEC vehicles. Extensive simulations show reduced task execution time for task offloading with high reliability [ 28 ]. A mobility-aware task offloading scheme [ 29 ] and collaborative computation offloading and resource allocation optimization scheme [ 30 ] are proposed for computation offloading in MEC.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Task Coordination Using Multi-Hop Communication in Volunteer Computing-Based VANETs

Waheed

Shah

Khan

et al. 2021

Sensors

View full text Add to dashboard Cite

Computation offloading is a process that provides computing services to vehicles with computation sensitive jobs. Volunteer Computing-Based Vehicular Ad-hoc Networking (VCBV) is envisioned as a promising solution to perform task executions in vehicular networks using an emerging concept known as vehicle-as-a-resource (VaaR). In VCBV systems, offloading is the primary technique used for the execution of delay-sensitive applications which rely on surplus resource utilization. To leverage the surplus resources arising in periods of traffic congestion, we propose a hybrid VCBV task coordination model which performs the resource utilization for task execution in a multi-hop fashion. We propose an algorithm for the determination of boundary relay vehicles to minimize the requirement of placement for multiple road-side units (RSUs). We propose algorithms for primary and secondary task coordination using hybrid VCBV. Extensive simulations show that the hybrid technique for task coordination can increase the system utility, while the latency constraints are addressed.

show abstract

Section: Related Workmentioning

confidence: 99%

Hybrid Task Coordination Using Multi-Hop Communication in Volunteer Computing-Based VANETs

Waheed

Shah

Khan

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…The highlights are particle swarm optimization (PSO) [114], [115], [128], ant colony optimization (ACO) [75], [92], and genetic algorithm (GA) [77], [102], [149], [151]. Other examples of metaheuristics are bat algorithm [83] and iterated local search (ILS) [154].…”

Section: D: Metaheuristicmentioning

confidence: 99%

“…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%

See 1 more Smart Citation

Computation Offloading for Vehicular Environments: A Survey

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

“…In vehicular cloud computing, task offloading is inseparable from efficient resource allocation technology. Resources involved in the entire process of offloading the task of the vehicle to the vehicular cloud are mainly divided into the communication resources and the computing resources [6]. [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Joint Task Offloading and Resource Allocation Strategy for DiffServ in Vehicular Cloud System

Kang

Chen

Dai

2020

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

A vehicular cloud (VC) can reduce latency and improve resource utilization of the Internet of vehicles by effectively using the underutilized computing resources of nearby vehicles. Although the task offloading of the VC enhances road safety and traffic management on the Internet of vehicles and meets the low-latency requirements for driving safety services on the Internet of vehicles business, there are still some key challenges such as the resource allocation mechanism of differentiated services (DiffServ) and task offloading mechanism of improving user experience. To address these issues, we study the task offloading and resource allocation strategy of the VC system where tasks generated by vehicles can be offloaded and executed cooperatively by vehicles in VC. Specifically, the computing task is further divided into independent subtasks and executed in different vehicles in VC to maximize the offloading utility. Considering the mobility of vehicles, the deadline of tasks, and the limited computing resources, we propose the optimization problem of task offloading in the VC system in the cause of improved user experience. To characterize the difference in service requirements resulting from the diversity of tasks, a DiffServ model focusing on the pricing of a task is utilized. The initial pricing of a task is tailored by the characteristics of the task and the uniqueness of the network status. In this model, tasks are sorted and processed in order according to task pricing, so as to optimize resource allocation. Numerical results show that the proposed scheme can effectively increase the resource utilization and task completion ratio.

show abstract

Joint communication and computing resource allocation in vehicular edge computing

Cited by 40 publications

References 23 publications

Hybrid Task Coordination Using Multi-Hop Communication in Volunteer Computing-Based VANETs

Hybrid Task Coordination Using Multi-Hop Communication in Volunteer Computing-Based VANETs

Computation Offloading for Vehicular Environments: A Survey

Joint Task Offloading and Resource Allocation Strategy for DiffServ in Vehicular Cloud System

Contact Info

Product

Resources

About