计算机科学
排队论
服务器
强化学习
任务(项目管理)
计算机网络
吞吐量
分布式计算
排队
排队延迟
钥匙(锁)
无线
人工智能
计算机安全
电信
经济
管理
作者
Zehan Jia,Zhenyu Zhou,Xiaoyan Wang,Shahid Mumtaz
标识
DOI:10.1109/icc42927.2021.9500852
摘要
Collaborative vehicular network is a key enabler to meet the stringent communication and computing requirements of user vehicles (UVs). A UV dynamically optimizes task offloading by exploiting its collaborations with edge servers and vehicular fog servers (VFSs). However, the optimization of task offloading in highly dynamic collaborative vehicular networks faces several challenges such as queuing delay guaranteeing, incomplete information, and dimensionality curse. In this paper, a Deep Reinforcement lEarning-based queue-Aware task offloading algorithM named DREAM is proposed to maximize the throughput of the UVs while satisfying the long-term queuing delay constraints in a best-effort way. Compared with existing task offloading algorithms, DREAM achieves superior performance in throughput, convergence, and queuing delay.
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