Optimized Placement of Symmetrical Service Function Chain in Network Function Virtualization

Nhat-Minh Dang-Quang1 and Myungsik Yoo2

  1. Department of Information Communication Convergence Technology, Soongsil University
    Seoul 06978, Korea
    minhdqn@soongsil.ac.kr
  2. School of Electronic Engineering, Soongsil University
    Seoul 06978, Korea
    myoo@ssu.ac.kr

Abstract

Network function virtualization (NFV) is one of the key technology enablers for actualizing 5G networks. With NFV, virtual network functions (VNFs) are linked together as a service function chain (SFC), which provides network functionality for the customer on demand. However, how to efficiently find a suitable placement for VNFs regarding the given objectives is an extremely difficult issue. The existing approaches assume that the SFC has a simple and asymmetrical pattern that is unsuitable to modeling a real system. We address this limitation by studying a VNF placement optimization problem with symmetrical SFCs that can support both symmetric and asymmetric traffic flows. This NP-hard problem is formulated as a mixed-integer linear programming (MILP) model. An iterative greedy-based heuristic is proposed to overcome the complexity of the MILP model. Extensive simulation results show that the proposed heuristic can obtain a near-optimal solution compared to MILP for a small-scale network, and at the same time, is superior to a traditional heuristic for a large-scale network.

Key words

Network function virtualization, multi-objective, VNF placement optimization, symmetric, heuristic

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS210920006D

Publication information

Volume 19, Issue 2 (June 2022)
Year of Publication: 2022
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

DownloadAvailable in PDF
Portable Document Format

How to cite

Dang-Quang, N., Yoo, M.: Optimized Placement of Symmetrical Service Function Chain in Network Function Virtualization. Computer Science and Information Systems, Vol. 19, No. 2, 803–827. (2022), https://doi.org/10.2298/CSIS210920006D