.png){kind=icon}
返回
Deep-Reinforcement-Learning-Based Offloading Scheduling for Vehicular Edge Computing
.png){kind=icon}
PRE
AI
DOI：10.1109/JIOT.2020.2978830.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Vehicular edge computing (VEC) is a new computing paradigm that has great potential to enhance the capability of vehicle terminals (VTs) to support resource-hungry in-vehicle applications with low latency and high energy efficiency. In this article, we investigate an important computation offloading scheduling problem in a typical VEC scenario, where a VT traveling along an expressway intends to schedule its tasks waiting in the queue to minimize the long-term cost in terms of a tradeoff between task latency and energy consumption. Due to diverse task characteristics, dynamic wireless environment, and frequent handover events caused by vehicle movements, an optimal solution should take into account both where to schedule (i.e., local computation or offloading) and when to schedule (i.e., the order and time for execution) each task. To solve such a complicated stochastic optimization problem, we model it by a carefully designed Markov decision process (MDP) and resort to deep reinforcement learning (DRL) to deal with the enormous state space. Our DRL implementation is designed based on the state-of-the-art proximal policy optimization (PPO) algorithm. A parameter-shared network architecture combined with a convolutional neural network (CNN) is utilized to approximate both policy and value function, which can effectively extract representative features. A series of adjustments to the state and reward representations are taken to further improve the training efficiency. Extensive simulation experiments and comprehensive comparisons with six known baseline algorithms and their heuristic combinations clearly demonstrate the advantages of the proposed DRL-based offloading scheduling method.
Keyword:
Task analysis
Servers
Processor scheduling
Schedules
Internet of Things
Edge computing
Wireless communication
Computation offloading
deep reinforcement learning (DRL)
mobile-edge computing (MEC)
task scheduling
vehicular edge computing (VEC)
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
机构
引用论文
.png){kind=icon}
Joint Load Balancing and Offloading in Vehicular Edge Computing and Networks车载边缘计算和网络中的联合负载均衡和卸载
PREAI
分享收藏
Study of bi-directional buck-boost converter topologies for application in electrical vehicle motor drives应用于电动汽车电机驱动的双向buck-boost变换器拓扑研究
PREAI
分享收藏
查看更多内容