Deep-Reinforcement-Learning-Based Offloading Scheduling for Vehicular Edge Computing

Zhan, Wenhan; Luo, Chunbo; Wang, Jin; Wang, Chao; Min, Geyong; Huang, Duan; Zhu, Qingxin

doi:10.1109/jiot.2020.2978830

Cited by 237 publications

(64 citation statements)

References 21 publications

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“…Some studies only considered offloading tasks to RSU or processing tasks locally. Han et al [24] established a MDP model for the problem, and optimized the offloading strategy with deep reinforcement learning. Although the study considers the change of vehicle's position in different time slots, it does not make full use of cooperative vehicle resources.…”

Section: Related Workmentioning

confidence: 99%

“…Dai [25] Xu [19] Liu [20] Guo [21] Our work (joint) 3 They mostly aimed at computation offloading of independent tasks [22,23], i.e., no data dependency among tasks of an application. 4 Most work only considered offloading tasks to RSUs or processing tasks directly on On-Board Unit (OBU) [24,25], without utilizing the idle computing resources of cooperative vehicles.…”

Section: Introductionmentioning

confidence: 99%

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Computation offloading strategy based on deep reinforcement learning for connected and autonomous vehicle in vehicular edge computing

Lin

et al. 2021

J Cloud Comp

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Connected and Automated Vehicle (CAV) is a transformative technology that has great potential to improve urban traffic and driving safety. Electric Vehicle (EV) is becoming the key subject of next-generation CAVs by virtue of its advantages in energy saving. Due to the limited endurance and computing capacity of EVs, it is challenging to meet the surging demand for computing-intensive and delay-sensitive in-vehicle intelligent applications. Therefore, computation offloading has been employed to extend a single vehicle’s computing capacity. Although various offloading strategies have been proposed to achieve good computing performace in the Vehicular Edge Computing (VEC) environment, it remains challenging to jointly optimize the offloading failure rate and the total energy consumption of the offloading process. To address this challenge, in this paper, we establish a computation offloading model based on Markov Decision Process (MDP), taking into consideration task dependencies, vehicle mobility, and different computing resources for task offloading. We then design a computation offloading strategy based on deep reinforcement learning, and leverage the Deep Q-Network based on Simulated Annealing (SA-DQN) algorithm to optimize the joint objectives. Experimental results show that the proposed strategy effectively reduces the offloading failure rate and the total energy consumption for application offloading.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computation offloading strategy based on deep reinforcement learning for connected and autonomous vehicle in vehicular edge computing

Lin

et al. 2021

J Cloud Comp

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show abstract

“…In order to improve this problem, Zhan et al. [ 22 ] proposed a scheme of disengagement strategy. Firstly, two artificial neural networks were used to approximate the behavior strategy and the target strategy respectively.…”

Section: Related Workmentioning

confidence: 99%

Towards Application-Driven Task Offloading in Edge Computing Based on Deep Reinforcement Learning

et al. 2021

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Edge computing is a new paradigm, which provides storage, computing, and network resources between the traditional cloud data center and terminal devices. In this paper, we concentrate on the application-driven task offloading problem in edge computing by considering the strong dependencies of sub-tasks for multiple users. Our objective is to joint optimize the total delay and energy generated by applications, while guaranteeing the quality of services of users. First, we formulate the problem for the application-driven tasks in edge computing by jointly considering the delays and the energy consumption. Based on that, we propose a novel Application-driven Task Offloading Strategy (ATOS) based on deep reinforcement learning by adding a preliminary sorting mechanism to realize the joint optimization. Specifically, we analyze the characteristics of application-driven tasks and propose a heuristic algorithm by introducing a new factor to determine the processing order of parallelism sub-tasks. Finally, extensive experiments validate the effectiveness and reliability of the proposed algorithm. To be specific, compared with the baseline strategies, the total cost reduction by ATOS can be up to 64.5% on average.

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“…In [34], a reinforcement learning algorithm was explored to address the delay-optimal task scheduling problem in cloud computing. In [35], a DRL-based approach was proposed to address the task scheduling and offloading problems in vehicular edge computing, while the latency demands were not considered. In [36], task scheduling with multiple resource allocation problems was tackled with DRL and imitation learning, where two objectives were defined.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning-Based Task Scheduling in IoT Edge Computing

Sheng

Chen

et al. 2021

Sensors

104

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Edge computing (EC) has recently emerged as a promising paradigm that supports resource-hungry Internet of Things (IoT) applications with low latency services at the network edge. However, the limited capacity of computing resources at the edge server poses great challenges for scheduling application tasks. In this paper, a task scheduling problem is studied in the EC scenario, and multiple tasks are scheduled to virtual machines (VMs) configured at the edge server by maximizing the long-term task satisfaction degree (LTSD). The problem is formulated as a Markov decision process (MDP) for which the state, action, state transition, and reward are designed. We leverage deep reinforcement learning (DRL) to solve both time scheduling (i.e., the task execution order) and resource allocation (i.e., which VM the task is assigned to), considering the diversity of the tasks and the heterogeneity of available resources. A policy-based REINFORCE algorithm is proposed for the task scheduling problem, and a fully-connected neural network (FCN) is utilized to extract the features. Simulation results show that the proposed DRL-based task scheduling algorithm outperforms the existing methods in the literature in terms of the average task satisfaction degree and success ratio.

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Deep-Reinforcement-Learning-Based Offloading Scheduling for Vehicular Edge Computing

Cited by 237 publications

References 21 publications

Computation offloading strategy based on deep reinforcement learning for connected and autonomous vehicle in vehicular edge computing

Computation offloading strategy based on deep reinforcement learning for connected and autonomous vehicle in vehicular edge computing

Towards Application-Driven Task Offloading in Edge Computing Based on Deep Reinforcement Learning

Deep Reinforcement Learning-Based Task Scheduling in IoT Edge Computing

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