A Deep Learning-based Virtual Network Function Placement Approach in NFV-enabled Networks

被引：0

作者：

Yue, Yi ^{[1
,2
]}

Sun, Shiding ^{[1
,2
]}

Tang, Xiongyan ^{[1
,2
]}

Zhang, Zhiyan ^{[1
,2
]}

Yang, Wencong ^{[1
,2
,3
]}

机构：

[1] China Unicom Res Inst, Beijing, Peoples R China

[2] Natl Engn Res Ctr Next Generat Internet Broadband, Beijing, Peoples R China

[3] Zhengzhou Univ, Sch Elect Engn, Zhengzhou, Peoples R China

来源：

2024 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC 2024 | 2024年

关键词：

Network Function Virtualization; VNF Placement; SFC Chaining; Deep Learning;

D O I：

10.1109/WCNC57260.2024.10571005

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

The emergence of Software-Defined Networks (SDN) and Network Function Virtualization (NFV) has made Service Function Chain (SFC) a popular method for delivering network services. This innovative computing and networking paradigm allows Virtual Network Functions (VNFs) to be cost-effectively deployed on a network of physical equipment flexibly and elastically. Traffic can be directed as needed by linking VNFs as an SFC. However, the current algorithms for VNF placement computation and traffic steering in SFC are often complex, unscalable, and time-consuming. This paper investigates the VNF placement and SFC chaining problem in NFV-enabled networks. To obtain the VNF placement solution that maximizes network resource utilization, we formulate the problem as a Binary Integer Programming (BIP) model. Additionally, we introduce a novel Deep Learning-based VNF Placement Algorithm (DLVPA) that uses an intelligent node selection network to place VNFs for SFC requests. Performance evaluations demonstrate that DLVPA can effectively improve network resource utilization and achieve high solution computation time efficiency.

引用

页数：6

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