سال انتشار:
2020
عنوان انگلیسی مقاله:
‘‘DRL + FL’’: An intelligent resource allocation model based on deep reinforcement learning for Mobile Edge Computing
ترجمه فارسی عنوان مقاله:
"DRL + FL": یک مدل تخصیص منابع هوشمند مبتنی بر یادگیری تقویت عمیق برای محاسبات لبه تلفن همراه
منبع:
Sciencedirect - Elsevier - Computer Communications, 160 (2020) 14-24. doi:10.1016/j.comcom.2020.05.037
نویسنده:
Nanliang Shan ∗, Xiaolong Cui, Zhiqiang Gao
چکیده انگلیسی:
With the emergence of a large number of computation-intensive and time-sensitive applications, smart terminal
devices with limited resources can only run the model training part of most intelligent applications in the
cloud, so a large amount of training data needs to be uploaded to the cloud. This is an important cause
of core network communication congestion and poor Quality-of-Experience (QoE) of user. As an important
extension and supplement of cloud computing, Mobile Edge Computing (MEC) sinks computing and storage
resources from the cloud to the vicinity of User Mobile Devices (UMDs), greatly reducing service latency
and alleviating the burden on core networks. However, due to the high cost of edge servers deployment and
maintenance, MEC also has the problems of limited network resources and computing resources, and the edge
network environment is complex and mutative. Therefore, how to reasonably allocate network resources and
computing resources in a changeable MEC environment has become a great aporia. To combat this issue,
this paper proposes an intelligent resource allocation model ‘‘DRL + FL’’. Based on this model, an intelligent
resource allocation algorithm DDQN-RA based on the emerging DRL algorithm framework DDQN is designed to
adaptively allocate network and computing resources. At the same time, the model integrates the FL framework
with the mobile edge system to train DRL agents in a distributed way. This model can well solve the problems
of uploading large amounts of training data via wireless channels, Non-IID and unbalance of training data
when training DRL agents, restrictions on communication conditions, and data privacy. Experimental results
show that the proposed ‘‘DRL + FL’’ model is superior to the traditional resource allocation algorithms SDR
and LOBO and the intelligent resource allocation algorithm DRLRA in three aspects: minimizing the average
energy consumption of the system, minimizing the average service delay, and balancing resource allocation.
Keywords: Mobile edge computing | Intelligent resource allocation | Deep reinforcement learning | Federated learning
قیمت: رایگان
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