Dynamic Service Function Chain Embedding for NFV-Enabled IoT: A Deep Reinforcement Learning Approach

被引:99
作者
Fu, Xiaoyuan [1 ]
Yu, F. Richard [2 ]
Wang, Jingyu [1 ]
Qi, Qi [1 ]
Liao, Jianxin [1 ]
机构
[1] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China
[2] Carleton Univ, Dept Syst & Comp Engn, Ottawa, ON K1S 5B6, Canada
基金
中国国家自然科学基金;
关键词
IoT; NFV; SFC embedding; deep Q-learning; PERFORMANCE OPTIMIZATION; RESOURCE-ALLOCATION; JOINT OPTIMIZATION; SMART CITIES; MANAGEMENT; NETWORKS; SYSTEMS; PLACEMENT; INTERNET; GAME;
D O I
10.1109/TWC.2019.2946797
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The Internet of things (IoT) is becoming more and more flexible and economical with the advancement in information and communication technologies. However, IoT networks will be ultra-dense with the explosive growth of IoT devices. Network function virtualization (NFV) emerges to provide flexible network frameworks and efficient resource management for the performance of IoT networks. In NFV-enabled IoT infrastructure, service function chain (SFC) is an ordered combination of virtual network functions (VNFs) that are related to each other based on the logic of IoT applications. However, the embedding process of SFC to IoT networks is becoming a big challenge due to the dynamic nature of IoT networks and the abundance of IoT terminals. In this paper, we decompose the complex VNFs into smaller virtual network function components (VNFCs) to make more effective decisions since VNF nodes and IoT network devices are usually heterogeneous. In addition, a deep reinforcement learning (DRL) based scheme with experience replay and target network is proposed as a solution that can efficiently handle complex and dynamic SFC embedding scenarios in IoT. Our simulations consider different types of IoT network topologies. The simulation results present the efficiency of the proposed dynamic SFC embedding scheme.
引用
收藏
页码:507 / 519
页数:13
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