-
Advanced Search

Citation: Lun TANG, Peipei ZHAO, Guofan ZHAO, Qianbin CHEN. Virtual Network Function Migration Algorithm Based on Deep Belief Network Prediction of Resource Requirements[J]. Journal of Electronics and Information Technology, ;2019, 41(6): 1397-1404. doi: 10.11999/JEIT180666 shu

Virtual Network Function Migration Algorithm Based on Deep Belief Network Prediction of Resource Requirements

  • Corresponding author: Lun TANG, tangl@cqupt.edu.cn
  • Received Date: 2018-07-05
    Accepted Date: 2019-01-28
    Available Online: 2019-06-01

Figures(12) / Tables(3)

  • To solve the problem of real-time migration of Virtual Network Function (VNF) caused by lacking effective prediction in 5G network, a VNF migration algorithm based on deep belief network prediction of resource requirements is proposed. The algorithm builds firstly a system cost evaluation model integrating bandwidth cost and migration cost,and then designs a deep belief network prediction algorithm based on online learning which adopts adaptive learning rate and introduces multi-task learning mode to predict future resource requirements. Finally, based on the prediction result as well as the perception of network topology and resources, the VNFs are migrated to the physical nodes that meet the resource threshold constraints through greedy selection algorithm with the goal to optimize system cost,and then a migration mechanism based on tabu search is proposed to further optimize the migration strategy.The simulation results show that the prediction model can obtain good prediction results and adaptive learning rate accelerates the convergence speed of the training network.Moreover, the combination with the migration algorithm reduces effectively system cost and the number of Service Level Agreements (SLA) violations during the migration process, and improves the performance of network services.
  • 加载中
    1. [1]

      MAHMOOD N H, LAURIDSEN M, BERARDINELLI G, et al. Radio resource management techniques for eMBB and mMTC services in 5G dense small cell scenarios[C]. Proceedings of the 84th Vehicular Technology Conference, Montreal, Canada, 2017: 1–5.

    2. [2]

      唐伦, 张亚, 梁荣, 等. 基于网络切片的网络效用最大化虚拟资源分配算法[J]. 电子与信息学报, 2017, 39(8): 1812–1818. doi: 10.11999/JEIT161322
      TANG Lun, ZHANG Ya, LIANG Rong, et al. Virtual resource allocation algorithm for network utility maximization based on network slicing[J]. Journal of Electronics &Information Technology, 2017, 39(8): 1812–1818. doi: 10.11999/JEIT161322

    3. [3]

      RAHMAN M M, DESPINS C, and AFFES S. Design optimization of wireless access virtualization based on cost & QoS trade-off utility maximization[J]. IEEE Transactions on Wireless Communications, 2016, 15(9): 6146–6162. doi: 10.1109/TWC.2016.2580505

    4. [4]

      QU Long, ASSI C, SHABAN K, et al. A reliability-aware network service chain provisioning with delay guarantees in NFV-enabled enterprise datacenter networks[J]. IEEE Transactions on Network and Service Management, 2017, 14(3): 554–568. doi: 10.1109/TNSM.2017.2723090

    5. [5]

      RIGGIO R, BRADAI A, HARUTYUNYAN D, et al. Scheduling wireless virtual networks functions[J]. IEEE Transactions on Network and Service Management, 2016, 13(2): 240–252. doi: 10.1109/TNSM.2016.2549563

    6. [6]

      LUIZELLI M C, BAYS L R, BURIOL L S, et al. Piecing together the NFV provisioning puzzle: Efficient placement and chaining of virtual network functions[C]. Proceedings of 2015 IFIP/IEEE International Symposium on Integrated Network Management, Ottawa, Canada, 2015: 98–106.

    7. [7]

      XIA Jing, CAI Zhiping, and XU Ming. Optimized virtual network functions migration for NFV[C]. Proceedings of 2016 IEEE International Conference on Parallel and Distributed Systems, Wuhan, China, 2016: 340–346.

    8. [8]

      ERAMO V, MIUCCI E, AMMAR M, et al. An approach for service function chain routing and virtual function network instance migration in network function virtualization architectures[J]. IEEE/ACM Transactions on Networking, 2017, 25(4): 2008–2025. doi: 10.1109/TNET.2017.2668470

    9. [9]

      MIJUMBI R, HASIJA S, DAVY S, et al. Topology-aware prediction of virtual network function resource requirements[J]. IEEE Transactions on Network and Service Management, 2017, 14(1): 106–120. doi: 10.1109/TNSM.2017.2666781

    10. [10]

      DEUTSCH J and HE D. Using deep learning-based approach to predict remaining useful life of rotating components[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018, 48(1): 11–20. doi: 10.1109/TSMC.2017.2697842

    11. [11]

      PATI J, KUMAR B, MANJHI D, et al. A comparison among ARIMA, BP-NN, and MOGA-NN for software clone evolution prediction[J]. IEEE Access, 2017, 5: 11841–11851. doi: 10.1109/ACCESS.2017.2707539

    12. [12]

      GIL H J and BOTERO J F. Resource allocation in NFV: A comprehensive survey[J]. IEEE Transactions on Network and Service Management, 2016, 13(3): 518–532. doi: 10.1109/TNSM.2016.2598420

    13. [13]

      MA Wenrui, MEDINA C, and PAN Deng. Traffic-aware placement of NFV middleboxes[C]. Proceedings of 2015 IEEE Global Communications Conference, San Diego, USA, 2015: 1–6. doi: 10.1109/GLOCOM.2015.7417851.

    14. [14]

      CARUANA R. Multitask learning[J]. Machine Learning, 1997, 28(1): 41–75. doi: 10.1023/a:1007379606734

    15. [15]

      MASHINCHI M H, MASHINCHI M R, and MASHINCHI M. Tabu search solution for fuzzy linear programming[C]. Proceedings of the 7th IEEE/ACIS International Conference on Computer and Information Science, Portland, USA, 2008: 82–87.

  • 加载中
    1. [1]

      Hongyun YANGFengyan WANG . Meteorological Radar Noise Image Semantic Segmentation Method Based on Deep Convolutional Neural Network. Journal of Electronics and Information Technology, 2019, 41(0): 1-9. doi: 10.11999/JEIT190098

    2. [2]

      Fei WANGShichao WUShaolin LIUYahui ZHANGYing WEI . Driver Fatigue Detection Through Deep Transfer Learning in an Electroencephalogram-based System. Journal of Electronics and Information Technology, 2019, 41(0): 1-9. doi: 10.11999/JEIT180900

    3. [3]

      Xiaoheng ZHANGYongming LIPin WANGXiaoping ZENGFang YANYanling ZHANGOumei CHENG . Classification Algorithm of Parkinson’s Disease Based on Convolutional Sparse Transfer Learning and Sample/Feature Parallel Selection. Journal of Electronics and Information Technology, 2019, 41(7): 1641-1649. doi: 10.11999/JEIT180792

    4. [4]

      Lun TANGYannan WEIRunlin MAXiaoyu HEQianbin CHEN . Online Learning-based Virtual Resource Allocation for Network Slicing in Virtualized Cloud Radio Access Network. Journal of Electronics and Information Technology, 2019, 41(7): 1533-1539. doi: 10.11999/JEIT180771

    5. [5]

      Yunjie GUYuxiang HUJichao XIE . A Spatial and Temporal Optimal Method of Service Function Chain Orchestration Based on Overlay Network Structure. Journal of Electronics and Information Technology, 2019, 0(0): 1-9. doi: 10.11999/JEIT190145

    6. [6]

      Xiaoqiang ZHAOZhaoyang SONG . Super-Resolution Reconstruction of Deep Residual Network with Multi-Level Skip Connections. Journal of Electronics and Information Technology, 2019, 41(0): 1-8. doi: 10.11999/JEIT190036

    7. [7]

      Ruyan WANGHongjuan LIDapeng WUHongxia LI . Semi-Markov Decision Process-based Resource Allocation Strategy for Virtual Sensor Network. Journal of Electronics and Information Technology, 2019, 41(0): 1-8. doi: 10.11999/JEIT190016

    8. [8]

      Yuze SUXiangru MENGQiaoyan KANGXiaoyang HAN . Core Link Aware Survivable Virtual Network Link Protection Method. Journal of Electronics and Information Technology, 2019, 41(7): 1587-1593. doi: 10.11999/JEIT180737

    9. [9]

      Changyu HULing WANGDongqiang ZHU . Sparse ISAR Imaging Exploiting Dictionary Learning. Journal of Electronics and Information Technology, 2019, 41(7): 1735-1742. doi: 10.11999/JEIT180747

    10. [10]

      Yang ZHOUJiayi WUYu LUHaibing YIN . Depth Map Error Concealment for 3D High Efficiency Video Coding. Journal of Electronics and Information Technology, 2019, 41(0): 1-8. doi: 10.11999/JEIT180926

    11. [11]

      Yang LIWeitao ZHANGShuntian LOU . Deep Convolution Blind Separation of Acoustic Signals Based on Joint Diagonalization. Journal of Electronics and Information Technology, 2019, 41(0): 1-6. doi: 10.11999/JEIT190067

    12. [12]

      Ying CHENXiaoyue XU . Matrix Metric Learning for Person Re-identification Based on Bidirectional Reference Set. Journal of Electronics and Information Technology, 2019, 41(0): 1-9. doi: 10.11999/JEIT190159

    13. [13]

      Li WANGYifan CAOGaoming DUGuanyu LIUXiaolei WANGDuoli ZHANG . A Low-latency Depth Modelling Mode-1 Encoder in 3D-high Efficiency Video Coding Standard. Journal of Electronics and Information Technology, 2019, 41(7): 1625-1632. doi: 10.11999/JEIT180798

    14. [14]

      Shibao LIShengzhi WANGJianhang LIUTingpei HUANGXin ZHANG . Semi-supervised Indoor Fingerprint Database Construction Method Based on the Nonhomogeneous Distribution Characteristic of Received Signal Strength. Journal of Electronics and Information Technology, 2019, 41(0): 1-8. doi: 10.11999/JEIT180599

    15. [15]

      Yan ZHANGJianhua CHENMeng TANG . Distributed LT Codes on Multiple Layers Networks. Journal of Electronics and Information Technology, 2019, 41(7): 1548-1554. doi: 10.11999/JEIT180804

    16. [16]

      Wei WANGKaili ZHOUYichang WANGGuang WANGJun YUAN . Design of Convolutional Neural Networks Accelerator Based on Fast Filter Algorithm. Journal of Electronics and Information Technology, 2019, 41(0): 1-7. doi: 10.11999/JEIT190037

    17. [17]

      Guangwu CHENJianhao CHENGJuhua YANGHao LIULinjing ZHANG . Improved Neural Network Enhanced Navigation System of Adaptive Unsented Kalman Filter. Journal of Electronics and Information Technology, 2019, 41(7): 1766-1773. doi: 10.11999/JEIT181171

    18. [18]

      Yuan SUNChunguo LIYongming HUANGLüxi YANG . Optimal Energy-efficient Design for Cache-based Cloud Radio Access Network. Journal of Electronics and Information Technology, 2019, 41(7): 1525-1532. doi: 10.11999/JEIT180722

    19. [19]

      Ningning QINLei JINJian XUFan XULe YANG . Neighbor Information Constrained Node Scheduling in Stochastic Heterogeneous Wireless Sensor Networks. Journal of Electronics and Information Technology, 2019, 41(0): 1-8. doi: 10.11999/JEIT190094

    20. [20]

      Chunsheng TIANZhihong QIANXin WANGXue WANG . Research on Channel Selection and Power Control Strategy for D2D Networks. Journal of Electronics and Information Technology, 2019, 41(0): 1-7. doi: 10.11999/JEIT190149

Metrics
  • PDF Downloads(31)
  • Abstract views(262)
  • HTML views(146)
  • Cited By(0)

通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

/

DownLoad:  Full-Size Img  PowerPoint
Return