Joint Offloading Scheduling and Resource Allocation in Vehicular Edge Computing: A Two Layer Solution

Vehicular Edge Computing (VEC) is a promising paradigm for autonomous driving. It can reduce delay and energy consumption of tasks. The problem of joint task offloading scheduling and resource allocation in VEC is a challenge issue. In this paper, we investigate the problem of joint task offloading,...

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Vydané v:	IEEE transactions on vehicular technology Ročník 72; číslo 3; s. 1 - 12
Hlavní autori:	Gao, Jian, Kuang, Zhufang, Gao, Jie, Zhao, Lian
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.03.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Completion time Computation offloading Deep Q-Network Delay Delays Edge computing Energy consumption Gradient Descent Mixed integer offloading scheduling Optimization Processor scheduling Resource allocation Resource management Resource scheduling Scheduling Servers Task analysis Task scheduling time-varying channel Vehicular edge computing
ISSN:	0018-9545, 1939-9359
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Abstract	Vehicular Edge Computing (VEC) is a promising paradigm for autonomous driving. It can reduce delay and energy consumption of tasks. The problem of joint task offloading scheduling and resource allocation in VEC is a challenge issue. In this paper, we investigate the problem of joint task offloading, task scheduling, and resource allocation in VEC, and the fast changing channel between a vehicle and an edge server. A target problem of joint considering task offloading scheduling, resource allocation and time-varying channel in VEC is formulated. The goal is to minimize the delay and energy consumption of tasks to guarantee the Quality of Service (QoS) of VEC. Constraints on the completion time, the energy consumption, and the computing capability are considered for each task. The resulting mixed integer optimization problem is decomposed into a two-layer optimization problem. In the upper layer, we use a Deep Q-Network (DQN) to solve the task offloading scheduling problem. In the lower level, the CPU frequency allocation is determined using the Gradient Descent (GD) method. Numerical results illustrate that the proposed algorithm can minimize the delay and energy consumption of VEC for different network parameter settings.
AbstractList	Vehicular Edge Computing (VEC) is a promising paradigm for autonomous driving. It can reduce delay and energy consumption of tasks. The problem of joint task offloading scheduling and resource allocation in VEC is a challenge issue. In this paper, we investigate the problem of joint task offloading, task scheduling, and resource allocation in VEC, and the fast changing channel between a vehicle and an edge server. A target problem of joint considering task offloading scheduling, resource allocation and time-varying channel in VEC is formulated. The goal is to minimize the delay and energy consumption of tasks to guarantee the Quality of Service (QoS) of VEC. Constraints on the completion time, the energy consumption, and the computing capability are considered for each task. The resulting mixed integer optimization problem is decomposed into a two-layer optimization problem. In the upper layer, we use a Deep Q-Network (DQN) to solve the task offloading scheduling problem. In the lower level, the CPU frequency allocation is determined using the Gradient Descent (GD) method. Numerical results illustrate that the proposed algorithm can minimize the delay and energy consumption of VEC for different network parameter settings.
Author	Kuang, Zhufang Gao, Jie Zhao, Lian Gao, Jian
Author_xml	– sequence: 1 givenname: Jian surname: Gao fullname: Gao, Jian organization: School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, China – sequence: 2 givenname: Zhufang orcidid: 0000-0002-1445-3217 surname: Kuang fullname: Kuang, Zhufang organization: School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, China – sequence: 3 givenname: Jie orcidid: 0000-0001-6095-2968 surname: Gao fullname: Gao, Jie organization: School of Information Technology, Carleton University, Ottawa, ON, Canada – sequence: 4 givenname: Lian orcidid: 0000-0002-5602-1738 surname: Zhao fullname: Zhao, Lian organization: Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University (formerly Ryerson University), Toronto, ON, Canada
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Snippet	Vehicular Edge Computing (VEC) is a promising paradigm for autonomous driving. It can reduce delay and energy consumption of tasks. The problem of joint task...
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SubjectTerms	Algorithms Completion time Computation offloading Deep Q-Network Delay Delays Edge computing Energy consumption Gradient Descent Mixed integer offloading scheduling Optimization Processor scheduling Resource allocation Resource management Resource scheduling Scheduling Servers Task analysis Task scheduling time-varying channel Vehicular edge computing
Title	Joint Offloading Scheduling and Resource Allocation in Vehicular Edge Computing: A Two Layer Solution
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