计算机科学
GSM演进的增强数据速率
计算
强化学习
服务器
资源(消歧)
人工智能
计算机网络
算法
作者
Tian Huang,Xiaolong Xu,Lianyong Qi,Xuyun Zhang,Wanchun Dou,Shui Yu,Qiang Ni
出处
期刊:IEEE Transactions on Vehicular Technology
[Institute of Electrical and Electronics Engineers]
日期:2021-12-01
卷期号:70 (12): 13281-13293
被引量:22
标识
DOI:10.1109/tvt.2021.3121096
摘要
Currently, self-driving, emerging as a key automatic application, has brought a huge potential for the provision of in-vehicle services (e.g., automatic path planning) to mitigate urban traffic congestion and enhance travel safety. To provide high-quality vehicular services with stringent delay constraints, edge computing (EC) enables resource-hungry self-driving vehicles (SDVs) to offload computation-intensive tasks to the edge servers (ESs). In addition, caching highly reusable contents decreases the redundant transmission time and improves the quality of services (QoS) of SDVs, which is envisioned as a supplement to the computation offloading. However, the high mobility and time-varying requests of SDVs make it challenging to provide reliable offloading decisions while guaranteeing the resource utilization of content caching. To this end, in this paper we propose a co llaborative com p utation offlo a ding and c ont e nt caching method, named CoPace, by leveraging deep reinforcement learning (DRL) in EC for self-driving system. Specifically, we first introduce OSTP to predict the future time-varying content popularity, taking into account the temporal-spatial attributes of requests. Moreover, a DRL-based algorithm is developed to jointly optimize the offloading and caching decisions, as well as the resource allocation (i.e., computing and communication resources) strategies. Extensive experiments with real-world datasets in Shanghai, China, are conducted to evaluate the performance, which demonstrates that CoPace is both effective and well-performed.
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