计算机科学
强化学习
可扩展性
分布式计算
软件部署
虚拟化
虚拟网络
计算机网络
云计算
人工智能
软件工程
操作系统
作者
Yuchen Zhu,Haipeng Yao,Tianle Mai,Wenji He,Ni Zhang,Mohsen Guizani
出处
期刊:IEEE Internet of Things Journal
[Institute of Electrical and Electronics Engineers]
日期:2022-09-01
卷期号:9 (17): 15674-15684
被引量:12
标识
DOI:10.1109/jiot.2022.3151134
摘要
Nowadays, the compelling applications of the Internet of Things (IoT) bring unexpected economic benefits to our daily lives. But at the same time, it also poses huge challenges to service providers. Diverse proprietary hardware (i.e., firewall and code conversion) have to be deployed in networks for meeting different applications’ requirements. Recently, network functions virtualization (NFV) is considered a promising technique. In the NFV-enabled architecture, network services can be implemented via a set of orderly virtual network functions (VNFs) on standardized compute nodes, which is termed service function chains (SFCs). However, with the explosion of IoT applications, embedding multiple SFCs in a shared NFV-enabled infrastructure becomes a challenging problem. Centralized schemes suffer from the scalability and private issue, while distributed schemes suffer from the nonconvergence problem. In this article, we propose a hybrid intelligent control architecture, which adopts the centralized training and distributed execution paradigm. A centralized critic is introduced to ease the training process of the distributed network nodes. Besides, considering the competitive behavior of users, we formulate the resource allocation problem as a multiuser competition game model. Based on this, we proposed a multiagent reinforcement learning-based SFCs deployment algorithm.
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