Joint Optimization of Computation Offloading and Task Scheduling in Vehicular Edge Computing Networks

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

doi:10.1109/access.2020.2965620

Cited by 74 publications

(42 citation statements)

References 26 publications

(24 reference statements)

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“…Recently, a multi-user, multi-server vehicular edge computing environment was studied in [26], where the computational offloading and task scheduling were jointly formulated as a mixed-integer non-linear programming problem whose objective was to maximize system utility. In addition, a hybrid intelligent with a low-complexity algorithm was developed to obtain the near-optimal solution.…”

Section: A Conventional-based Methodsmentioning

confidence: 99%

Advanced Deep Learning-Based Computational Offloading for Multilevel Vehicular Edge-Cloud Computing Networks

et al. 2020

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The promise of low latency connectivity and efficient bandwidth utilization has driven the recent shift from vehicular cloud computing (VCC) towards vehicular edge computing (VEC). This paper presents an advanced deep learning-based computational offloading algorithm for multilevel vehicular edge-cloud computing networks. To conserve energy and guarantee the efficient utilization of shared resources among multiple vehicles, an integration model of computational offloading, and resource allocation is formulated as a binary optimization problem to minimize the total cost of the entire system in terms of time and energy. However, this problem is considered NP-hard and it is computationally prohibitive to solve this type of problem, particularly for large-scale vehicles, due to the curse-of-dimensionality problem. Therefore, an equivalent reinforcement learning form is generated and we propose a distributed deep learning algorithm to find the near-optimal computational offloading decisions in which a set of deep neural networks are used in parallel. Finally, simulation results show that the proposed algorithm can exhibit fast convergence and significantly reduce the overall consumption of an entire system compared to the benchmark solutions.INDEX TERMS Computation offloading, vehicular edge-cloud computing, autonomous vehicles, 5G, resource allocation, deep reinforcement learning.

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Section: A Conventional-based Methodsmentioning

confidence: 99%

Advanced Deep Learning-Based Computational Offloading for Multilevel Vehicular Edge-Cloud Computing Networks

et al. 2020

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“…From the perspective of offloading strategy optimization goal. Sun et al [24] formulated a mixed-integer nonlinear programming problem to maximize the system's offloading utility with the joint optimization of offloading decision and task scheduling. Zhang et al [25] proposed an offloading framework by considering the heterogeneous and the mobility of vehicles.…”

Section: Related Workmentioning

confidence: 99%

“…Computing offloading of VEC is a current research hotspot [12][13][14][15][16]. However, there are some deficiencies hidden in the existing works.…”

Section: Introductionmentioning

confidence: 99%

A Context-aware Task Offloading Scheme in Collaborative Vehicular Edge Computing Systems

2021

KSII TIIS

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With the development of mobile edge computing (MEC), some late-model application technologies, such as self-driving, augmented reality (AR) and traffic perception, emerge as the times require. Nevertheless, the high-latency and low-reliability of the traditional cloud computing solutions are difficult to meet the requirement of growing smart cars (SCs) with computing-intensive applications. Hence, this paper studies an efficient offloading decision and resource allocation scheme in collaborative vehicular edge computing networks with multiple SCs and multiple MEC servers to reduce latency. To solve this problem with effect, we propose a context-aware offloading strategy based on differential evolution algorithm (DE) by considering vehicle mobility, roadside units (RSUs) coverage, vehicle priority. On this basis, an autoregressive integrated moving average (ARIMA) model is employed to predict idle computing resources according to the base station traffic in different periods. Simulation results demonstrate that the practical performance of the context-aware vehicular task offloading (CAVTO) optimization scheme could reduce the system delay significantly.

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“…As the WAVE architecture connects vehicles with other devices via wireless communication, some works use it to enable computation offloading in vehicular scenarios, such as: [75], [76], [77], and [78].…”

Section: ) Technologymentioning

confidence: 99%

“…Among the selected works, a great majority employ metaheuristics for solving optimization problems. 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%

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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Joint Optimization of Computation Offloading and Task Scheduling in Vehicular Edge Computing Networks

Cited by 74 publications

References 26 publications

Advanced Deep Learning-Based Computational Offloading for Multilevel Vehicular Edge-Cloud Computing Networks

Advanced Deep Learning-Based Computational Offloading for Multilevel Vehicular Edge-Cloud Computing Networks

A Context-aware Task Offloading Scheme in Collaborative Vehicular Edge Computing Systems

Computation Offloading for Vehicular Environments: A Survey

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