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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Bibliographic Details
Published in:IEEE transactions on vehicular technology Vol. 72; no. 3; pp. 1 - 12
Main Authors: Gao, Jian, Kuang, Zhufang, Gao, Jie, Zhao, Lian
Format: Journal Article
Language:English
Published: New York IEEE 01.03.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2022.3220571