A Multi-domain Virtual Network Embedding Algorithm with Delay Prediction

被引:0
作者
Zhang, Peiying [1 ]
Pang, Xue [1 ]
Ni, Yongjing [2 ,3 ]
Yao, Haipeng [4 ]
Li, Xin [5 ]
机构
[1] China Univ Petr East China, Coll Comp Sci & Technol, Qingdao 266580, Peoples R China
[2] Yanshan Univ, Sch Informat Sci & Engn, Qinhuangdao 066000, Hebei, Peoples R China
[3] Hebei Univ Sci & Technol, Sch Informat Sci & Engn, Shijiazhuang 050000, Hebei, Peoples R China
[4] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China
[5] Peoples Publ Secur Univ China, Coll Informat Technol & Cyber Secur, Beijing 100038, Peoples R China
关键词
Network virtualization; virtual network embedding; substrate network; delay sensitive; particle swarm optimization; INDUSTRIAL INTERNET; ENERGY; ALLOCATION; THINGS;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Virtual network embedding (VNE) is a crucial part of network virtualization (NV), which aims to map the virtual networks (VNs) to a shared substrate network (SN). With the emergence of various delay-sensitive applications, how to improve the delay performance of the system has become a hot topic in academic circles. Based on extensive research, we proposed a multi-domain virtual network embedding algorithm based on delay prediction (DP-VNE). Firstly, the candidate physical nodes are selected by estimating the delay of virtual requests, then particle swarm optimization (PSO) algorithm is used to optimize the mapping process, so as to reduce the delay of the system. The simulation results show that compared with the other three advanced algorithms, the proposed algorithm can significantly reduce the system delay while keeping other indicators unaffected.
引用
收藏
页码:47 / 71
页数:25
相关论文
共 40 条
  • [1] [Anonymous], 2016, 24 INT C SOFTW TEL C
  • [2] A Novel Optimal Mapping Algorithm With Less Computational Complexity for Virtual Network Embedding
    Cao, Haotong
    Zhu, Yongxu
    Zheng, Gan
    Yang, Longxiang
    [J]. IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, 2018, 15 (01): : 356 - 371
  • [3] Cui H, 2013, INT S WIR PERS MULT
  • [4] Dynamic Service Function Chain Embedding for NFV-Enabled IoT: A Deep Reinforcement Learning Approach
    Fu, Xiaoyuan
    Yu, F. Richard
    Wang, Jingyu
    Qi, Qi
    Liao, Jianxin
    [J]. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2020, 19 (01) : 507 - 519
  • [5] Service Function Chain Embedding for NFV-Enabled IoT Based on Deep Reinforcement Learning
    Fu, Xiaoyuan
    Yu, F. Richard
    Wang, Jingyu
    Qi, Qi
    Liao, Jianxin
    [J]. IEEE COMMUNICATIONS MAGAZINE, 2019, 57 (11) : 102 - 108
  • [6] Geng Ruiwen, 2016, MINIATURE MICROCOMPU
  • [7] Software-Defined Networks with Mobile Edge Computing and Caching for Smart Cities: A Big Data Deep Reinforcement Learning Approach
    He, Ying
    Yu, F. Richard
    Zhao, Nan
    Leung, Victor C. M.
    Yin, Hongxi
    [J]. IEEE COMMUNICATIONS MAGAZINE, 2017, 55 (12) : 31 - 37
  • [8] Virtual network provisioning across multiple substrate networks
    Houidi, Ines
    Louati, Wajdi
    Ben Ameur, Walid
    Zeghlache, Djamal
    [J]. COMPUTER NETWORKS, 2011, 55 (04) : 1011 - 1023
  • [9] Energy-Efficient Machine-to-Machine (M2M) Communications in Virtualized Cellular Networks with Mobile Edge Computing (MEC)
    Li, Meng
    Yu, F. Richard
    Si, Pengbo
    Zhang, Yanhua
    [J]. IEEE TRANSACTIONS ON MOBILE COMPUTING, 2019, 18 (07) : 1541 - 1555
  • [10] Detail-preserving 3D Shape Modeling from Raw Volumetric Dataset via Hessian-constrained Local Implicit Surfaces Optimization
    Li, Shuai
    Yan, Dehui
    Li, Xiangyang
    Hao, Aimin
    Qin, Hong
    [J]. 2016 INTERNATIONAL CONFERENCE ON CYBERWORLDS (CW), 2016, : 25 - 32