Multi-objective enhanced particle swarm optimization in virtual network embedding

被引:23
作者
Zhang, Peiying [1 ,2 ,3 ]
Yao, Haipeng [1 ]
Fang, Chao [2 ,4 ]
Liu, Yunjie [1 ]
机构
[1] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Xitucheng Rd 10, Beijing 100876, Peoples R China
[2] Beijing Univ Technol, Beijing Adv Innovat Ctr Future Internet Technol, Pingleyuan 100, Beijing 100124, Peoples R China
[3] China Univ Petr East China, Coll Comp & Commun Engn, Changjiang West Rd 66, Qingdao 266580, Peoples R China
[4] Beijing Univ Technol, Coll Elect Informat & Control Engn, Pingleyuan 100, Beijing 100124, Peoples R China
基金
中国国家自然科学基金;
关键词
Network virtualization; Particle swarm optimization; Virtual network embedding; NODE;
D O I
10.1186/s13638-016-0669-2
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
In network virtualization, one of its core challenges lies in how to map the virtual networks (VNs) to the shared substrate network (SN) that is managed by an infrastructure provider, termed as the virtual network embedding problem. Prior studies on this problem only consider one objective, e.g., maximizing the revenues by mapping more VNs or minimizing the energy cost. In this paper, we addressed the virtual network embedding problem with these two objectives. We leverage niche particle swarm optimization technique to design a meta-heuristic algorithm to solve this problem. Extensive simulations illustrate that the efficiency of our proposed algorithm is better than the state-of-the-art algorithms in terms of both revenue and energy cost.
引用
收藏
页数:9
相关论文
共 50 条
[41]   Exact Multi-Objective Virtual Network Embedding in Cloud Environments [J].
Houidi, Ines ;
Louati, Wajdi ;
Zeghlache, Djamal .
COMPUTER JOURNAL, 2015, 58 (03) :403-415
[42]   A multi-objective particle swarm optimizer based on reference point for multimodal multi-objective optimization [J].
Li, Guosen ;
Zhou, Ting .
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2022, 107
[43]   An efficient hybrid multi-objective particle swarm optimization with a multi-objective dichotomy line search [J].
Xu, Gang ;
Yang, Yu-qun ;
Liu, Bin-Bin ;
Xu, Yi-hong ;
Wu, Ai-jun .
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, 2015, 280 :310-326
[44]   Neural network river forecasting with multi-objective fully informed particle swarm optimization [J].
Taormina, Riccardo ;
Chau, Kwok-wing .
JOURNAL OF HYDROINFORMATICS, 2015, 17 (01) :99-113
[45]   Multi-objective particle swarm optimization for uncertain reliability optimization problems [J].
Zhang, En-Ze ;
Chen, Qing-Wei .
Kongzhi yu Juece/Control and Decision, 2015, 30 (09) :1701-1705
[46]   Dynamic Particle Swarm Optimization to Solve Multi-objective Optimization Problem [J].
Urade, Hemlata S. ;
Patel, Rahila .
2ND INTERNATIONAL CONFERENCE ON COMMUNICATION, COMPUTING & SECURITY [ICCCS-2012], 2012, 1 :283-290
[47]   Particle swarm optimization algorithms for interval multi-objective optimization problems [J].
Zhang, En-Ze ;
Wu, Yi-Fei ;
Chen, Qing-Wei .
Kongzhi yu Juece/Control and Decision, 2014, 29 (12) :2171-2176
[48]   Geometric Particle Swarm Optimization for Multi-objective Optimization Using Decomposition [J].
Zapotecas-Martinez, Saul ;
Moraglio, Alberto ;
Aguirre, Hernan E. ;
Tanaka, Kiyoshi .
GECCO'16: PROCEEDINGS OF THE 2016 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, 2016, :69-76
[49]   An Improved Hybrid Multi-objective Particle Swarm Optimization Algorithm [J].
Zhou, Zuan ;
Dai, Guangming ;
Fang, Pan ;
Chen, Fangjie ;
Tan, Yi .
ADVANCES IN COMPUTATION AND INTELLIGENCE, PROCEEDINGS, 2008, 5370 :181-188
[50]   An Analysis of Particle Swarm Optimization of Multi-objective Knapsack Problem [J].
Liu, Zhuo .
2020 9TH INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY AND MANAGEMENT (ICITM 2020), 2020, :302-306