Task offloading for vehicular edge computing with edge-cloud cooperation

Dai, Fei; Lv, Guozhi; Mo, Qi; Xu, Weiheng; Huang, Bi

doi:10.1007/s11280-022-01011-8

Cited by 29 publications

(22 citation statements)

References 28 publications

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“…Coarse-grained task offloading refers to the task offloading schemes without the consideration of the decomposability and dependency of tasks. Dai et al [9] proposed an efficient task offloading approach based on DQN to minimize the processing delay of tasks, which jointly considered the edge-cloud opportunities and the convergence of deep reinforcement learning. Xu et al [8] presented a game theory-based service offloading approach to minimize tasks processing latency of users, where both predictions of traffic flow and the allocation of resources are considered.…”

Section: Related Workmentioning

confidence: 99%

“…In [19], the authors proposed a task offloading approach based on Q-learning to jointly optimize processing order and computing mode selection for minimizing the total processing delay. In [9], the authors proposed an efficient task offloading approach based on the deep Q-network (DQN) to minimize the processing delay of tasks, which jointly considered the edge-cloud opportunities and the convergence of deep reinforcement learning. Nevertheless, most of the existing works [9,19] quantized the decision variables into a discrete action space to achieve optimal computation offloading strategies due to Q-learning and DQN cannot solve the problem of continuous action space.…”

Section: Introductionmentioning

confidence: 99%

“…In [9], the authors proposed an efficient task offloading approach based on the deep Q-network (DQN) to minimize the processing delay of tasks, which jointly considered the edge-cloud opportunities and the convergence of deep reinforcement learning. Nevertheless, most of the existing works [9,19] quantized the decision variables into a discrete action space to achieve optimal computation offloading strategies due to Q-learning and DQN cannot solve the problem of continuous action space. As a result, it is not possible to obtain an optimal offloading strategy.…”

Section: Introductionmentioning

confidence: 99%

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Dependency-Aware Task Offloading for Vehicular Edge Computing with End-Edge-Cloud Collaborative Computing

Dai

Huang

et al. 2022

Preprint

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Vehicular edge computing (VEC) is emerging as a new computing paradigm to improve the quality of vehicular services and enhance the capabilities of vehicles. It aids vehicles execution tasks with low latency by deploying computing and storage resources close to the vehicle side. However, the traditional task offloading schemes waste the fine-grained offloading opportunities for subtasks by neglecting the decomposability and dependency of tasks. Furthermore, the continuous control problem caused by the learning-based offloading schemes should be taken into account. In this paper, we proposed an efficient dependency-aware task offloading scheme, which minimizes the average processing delay of tasks by fully utilizing the end-edge-cloud collaborative computing. Specifically, first, the directed acyclic graph (DAG) technique is utilized to model the inter-subtask dependency, which establishes the priority of subtask execution. Second, a task offloading algorithm based on Deep Deterministic Policy Gradient (DDPG) in Reinforcement Learning (RL) was proposed to obtain the optimal offloading strategy in a dynamic environment, which efficiently solves continuous control problems and helps reach fast convergence. Finally, we conduct extensive simulation experiments, and the experimental results show that the proposed dependency-aware task offloading scheme can achieve a good performance.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dependency-Aware Task Offloading for Vehicular Edge Computing with End-Edge-Cloud Collaborative Computing

Dai

Huang

et al. 2022

Preprint

Self Cite

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“…Vehicular edge computing (VEC) is proposed as a promising solution to solve the above problem, which integrates mobile edge computing (MEC) into IoV [9]. VEC can improve vehicle service quality [10][11][12] by deploying MEC servers' computation resources and storage resources close to vehicles.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, this type of task offloading with vehicleedge-cloud collaborative computing can obtain a low latency for various vehicular tasks. Such approaches have been extensively studied [9,16]. For example, Dai et al propose an offloading method for VEC, which offloads tasks based on vehicle-edge-cloud collaborative computing [9].…”

Section: Introductionmentioning

confidence: 99%

A collaborative computation and dependency-aware task offloading method for vehicular edge computing: a reinforcement learning approach

Dai

Huang

et al. 2022

J Cloud Comp

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Vehicular edge computing (VEC) is emerging as a new computing paradigm to improve the quality of vehicular services and enhance the capabilities of vehicles. It enables performing tasks with low latency by deploying computing and storage resources close to vehicles. However, the traditional task offloading schemes only focus on one-shot offloading, taking less into consideration task dependency. Furthermore, the continuous action space problem during task offloading should be considered. In this paper, an efficient dependency-aware task offloading scheme for VEC with vehicle-edge-cloud collaborative computation is proposed, where subtasks can be processed locally or can be offloaded to an edge server, or a cloud server for execution. Specifically, first, the directed acyclic graph (DAG) is utilized to model the dependency of subtasks. Second, a task offloading algorithm based on Deep Deterministic Policy Gradient (DDPG) was proposed to obtain the optimal offloading strategy in a vehicle-edge-cloud environment, which efficiently solves the continuous control problem and helps reach fast convergence. Finally, extensive simulation experiments have been conducted, and the experimental results show that the proposed scheme can improve performance by about 13.62% on average against three baselines.

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6G‐edge support of Internet of Autonomous Vehicles: A survey

Ibn‐Khedher,

Laroui,

Alfaqawi

et al. 2023

Trans Emerging Tel Tech

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With the commercial deployment of 5G mobile communication systems, 6G is proposed to meet the needs of new use cases including connected and autonomous vehicles (CAVs). In this article, we surveyed different 6G embracing technologies such as mobile edge computing, unmanned aerial vehicles, network function virtualization, software defined networking, and artificial intelligence that can enhance the Internet of Autonomous Vehicles (IoAV) network performance comparing to existing 5G enabled solutions. Then, we highlight the convergence path of 6G and IoAV to satisfy the next generation of CAV applications. Further, we propose 6G cellular communication to support the quality of next IoAV applications. Furthermore, the article attempts to study the serious challenges in the 6G‐IoAV research field and propose potential future directions.

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Task offloading for vehicular edge computing with edge-cloud cooperation

Cited by 29 publications

References 28 publications

Dependency-Aware Task Offloading for Vehicular Edge Computing with End-Edge-Cloud Collaborative Computing

Dependency-Aware Task Offloading for Vehicular Edge Computing with End-Edge-Cloud Collaborative Computing

A collaborative computation and dependency-aware task offloading method for vehicular edge computing: a reinforcement learning approach

6G‐edge support of Internet of Autonomous Vehicles: A survey

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