On Dynamic Service Function Chain Reconfiguration in IoT Networks

被引:25
|
作者
Liu, Yicen [1 ]
Lu, Yu [1 ]
Li, Xi [1 ]
Yao, Zhigang [1 ]
Zhao, Donghao [1 ]
机构
[1] Army Engn Univ, Shijiazhuang Campus, Shijiazhuang 050003, Hebei, Peoples R China
来源
IEEE INTERNET OF THINGS JOURNAL | 2020年 / 7卷 / 11期
基金
中国国家自然科学基金;
关键词
Internet of Things; Resource management; Quality of service; Cloud computing; Virtualization; Dynamic scheduling; Proposals; Deep Dyna-Q (DDQ) approach; discrete-time Markov decision process (DTMDP); Internet-of-Things (IoT) network; network function virtualization (NFV); service function chain (SFC) reconfiguration; MIGRATION; WORKLOAD; MODEL;
D O I
10.1109/JIOT.2020.2991753
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Network function virtualization (NFV) technology continues to gain more attention as a paradigm shift, and telecommunication services can be flexibly deployed and managed. Any service can be represented by a service function chain (SFC) that is a set of virtual network functions (VNFs) to be executed based on the strict order. The NFV-enabled SFCs applied in the future Internet-of-Things (IoT) networks emerge a challenging problem, particularly more and more IoT devices are trying to access their telecommunication services whenever and wherever, SFCs are needed to be dynamically and adaptively reconfigured, thus adapting to the service requests' dynamics for lower resource consumption and higher revenue for Internet service providers (ISPs). In this article, we study the SFC dynamic reconfiguration problem (SFC-DRP) in the IoT networks, a discrete-time Markov decision process (DTMDP)-based IoT SFC-DRP is formulated by guaranteeing the QoS and resource constraints. We subsequently propose a novel deep Dyna-Q (DDQ) approach to solve this model. Our proposal has been evaluated with the obtained results demonstrating an average CPU root-mean-square error (RMSE) of 0.17, compared to 0.75 obtained while using the original approach. Moreover, our proposed SFC reconfiguration technique can approximate the performance of the integer linear programming (ILP) model within a polynomial time, and outperform the existing benchmarks in terms of the reconfiguration overhead and the resource utilization ratio from service provisioning, respectively.
引用
收藏
页码:10969 / 10984
页数:16
相关论文
共 50 条
  • [1] A Multi-objective Service Function Chain Mapping Mechanism for IoT networks
    Han, Cong
    Xu, Siya
    Guo, Shaoyong
    Qiu, Xuesong
    Xiong, Ao
    Yu, Peng
    Guo, Kunya
    Guo, Dong
    [J]. 2019 15TH INTERNATIONAL WIRELESS COMMUNICATIONS & MOBILE COMPUTING CONFERENCE (IWCMC), 2019, : 72 - 77
  • [2] Dynamic Service Function Chain Orchestration for NFV/MEC-Enabled IoT Networks: A Deep Reinforcement Learning Approach
    Liu, Yicen
    Lu, Hao
    Li, Xi
    Zhang, Yang
    Xi, Leiping
    Zhao, Donghao
    [J]. IEEE INTERNET OF THINGS JOURNAL, 2021, 8 (09) : 7450 - 7465
  • [3] Service Function Chain Reconfiguration in 5G Core Networks Using Deep Learning
    Setayesh, Mehdi
    Wong, Vincent W. S.
    [J]. 2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), 2021,
  • [4] An Approach for Service Function Chain Reconfiguration in Network Function Virtualization Architectures
    Liu, Yicen
    Lu, Hao
    Li, Xi
    Zhao, Donghao
    [J]. IEEE ACCESS, 2019, 7 : 147224 - 147237
  • [5] Dynamic Orchestration Mechanism of Service Function Chain in Hybrid NFV Networks
    Liang, Xiao
    Huang, Xiaohong
    Li, Dandan
    Yang, Tianle
    [J]. 2018 ASIA COMMUNICATIONS AND PHOTONICS CONFERENCE (ACP), 2018,
  • [6] Multiobjective Genetic Algorithm for Fast Service Function Chain Reconfiguration
    Noghani, Kyoomars Alizadeh
    Kassler, Andreas
    Taheri, Javid
    Ohlen, Peter
    Curescu, Calin
    [J]. IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, 2023, 20 (03): : 3501 - 3522
  • [7] A reinforcement learning approach based on convolutional network for dynamic service function chain embedding in IoT
    Wang, Shuyi
    Yang, Longxiang
    [J]. INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS, 2022,
  • [8] Securing Dynamic Service Function Chain Orchestration in EC-IoT Using Federated Learning
    Wang, Shuyi
    Yang, Longxiang
    [J]. SENSORS, 2022, 22 (23)
  • [9] Service function chain migration with the long-term budget in dynamic networks
    Qin, Yudong
    Guo, Deke
    Luo, Lailong
    Zhang, Jingyu
    Xu, Ming
    [J]. COMPUTER NETWORKS, 2023, 223
  • [10] A Multi-Stage Approach for Virtual Network Function Migration and Service Function Chain Reconfiguration in NFV-enabled Networks
    Li, Biyi
    Cheng, Bo
    Chen, Junliang
    [J]. 2020 IEEE 13TH INTERNATIONAL CONFERENCE ON WEB SERVICES (ICWS 2020), 2020, : 207 - 215