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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.
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