Dependency-Aware Task Scheduling in Vehicular Edge Computing

Liu, Yujiong; Wang, Shangguang; Zhao, Qinglin; Du, Shaofu; Zhou, Aoying; Ma, Xiao; Yang, Fangchun

doi:10.1109/jiot.2020.2972041

Cited by 188 publications

(68 citation statements)

References 34 publications

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“…Vehicular densities are considered low when the vehicular density is approximately 11 vehicles/km in a highway scenario and 25 vehicles/km 2 in an urban scenario [207], [206]. As shown in Figure 7(d), low was the vehicular density most used in the offloading experiments [129], [96], [13].…”

Section: A: Lowmentioning

confidence: 99%

“…However, new and popular applications have emerged, such as applications based on artificial intelligence, augmented reality (AR), image-aided navigation, intelligent vehicle control, gaming, etc. Such applications demand massive computation and storage resources to handle complicated data processing and storage operations, and still have critical latency requirements [12], [13]. In addition, it is expected that with hundreds of sensors in future vehicles generating an enormous amount of data, there will be a lot of pressure on the computational resources of the vehicles.…”

Section: Introductionmentioning

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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Section: A: Lowmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computation Offloading for Vehicular Environments: A Survey

et al. 2020

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“…The probability is set based on the distance between the vehicle and the access point, application requirement, and communication overhead. In [23], Liu et al have discussed the task dependency relationship among multiple tasks, and have formulated the corresponding multi-task scheduling problem as an optimization problem. The problem is solved by prioritizing the tasks according to the completion time constraint and the process dependency requirement.…”

Section: B Edge Computing and Task Offloadingmentioning

confidence: 99%

Collaborative Vehicular Edge Computing Towards Greener ITS

Buda

Guleng

et al. 2020

IEEE Access

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In order to achieve a greener intelligent transport system (ITS), an efficient collaboration between vehicles is required to manage computation task processing with low latency. In this paper, we propose a collaborative edge computing scheme for vehicular Internet-of-things towards a greener ITS. The proposed scheme uses some vehicles as edge nodes, which are responsible for finding task processor nodes on behalf of a task requester node by considering the end-to-end task response time. The proposed scheme employs a two-stage approach where the first stage enables an efficient networking and computing architecture by forming vehicle clusters based on the edge architecture, and the second stage optimizes offloading tasks based on the architecture. We use realistic computer simulations to compare the proposed scheme with existing baselines, and show its superiority in terms of task offloading performance. INDEX TERMS Edge computing, vehicular Internet of Things, green ITS, collaborative intelligence.

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“…There are two commonly used structural representation methods to denote an application, namely the graph-based method [34] and the language-based method [35]. In particular, the graph-based method includes directed acyclic graph (DAG) [20][25] [27][28] [36] and Petri Net [37]. DAG based model is one of the most popular methods.…”

Section: A System Overviewmentioning

confidence: 99%

A Multiobjective Computation Offloading Algorithm for Mobile-Edge Computing

Song

Xing

Luo

et al. 2020

IEEE Internet Things J.

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In mobile edge computing (MEC), smart mobile devices (SMDs) with limited computation resources and battery lifetime can offload their computing-intensive tasks to MEC servers, thus to enhance the computing capability and reduce the energy consumption of SMDs. Nevertheless, offloading tasks to the edge incurs additional transmission time and thus higher execution delay. This paper studies the trade-off between the completion time of applications and the energy consumption of SMDs in MEC networks. The problem is formulated as a multiobjective computation offloading problem (MCOP), where the task precedence, i.e. ordering of tasks in SMD applications, is introduced as a new constraint in the MCOP. An improved multiobjective evolutionary algorithm based on decomposition (MOEA/D) with two performance enhancing schemes is proposed. 1) The problem-specific population initialization scheme uses a latency-based execution location initialization method to initialize the execution location (i.e. either local SMD or MEC server) for each task. 2) The dynamic voltage and frequency scaling based energy conservation scheme helps to decrease the energy consumption without increasing the completion time of applications. The simulation results clearly demonstrate that the proposed algorithm outperforms a number of state-of-the-art heuristics and meta-heuristics in terms of the convergence and diversity of the obtained nondominated solutions.

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Dependency-Aware Task Scheduling in Vehicular Edge Computing

Cited by 188 publications

References 34 publications

Computation Offloading for Vehicular Environments: A Survey

Computation Offloading for Vehicular Environments: A Survey

Collaborative Vehicular Edge Computing Towards Greener ITS

A Multiobjective Computation Offloading Algorithm for Mobile-Edge Computing

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