Virtual Network Function Migration Based on Dynamic Resource Requirements Prediction

Abstract

Network function virtualization (NFV) enables flexible deployment of virtual network function (VNF) in 5G mobile communication network. Due to the inherent dynamics of network flows, fluctuated resources are required to embedding VNFs. VNF migration has become a critical issue because of the time-varying resource requirements. In this paper, we propose a real-time VNF migration algorithm based on the deep belief network (DBN) to predict future resource requirements, which resolves the problem of lacking effective prediction in the existing methods. Firstly, we propose optimizing bandwidth utilization and migration overhead simultaneously in VNF migration. Then, to model the resource utilization that evolves over time, we adopt online learning with the assistant of offline training in the prediction mechanism, and further introduce multi-task learning (MTL) in our deep architecture in order to improve the prediction accuracy. Moreover, we utilize adaptive learning rate to speed up the convergence speed of DBN. For the migration, we design a topology-aware greedy algorithm with the goal to optimize system cost by taking full advantage of the prediction result. In addition, based on tabu search, the proposed migration mechanism is further optimized. Simulation results show that the proposed scheme can achieve a good performance in reducing system cost and improving the service level agreements (SLA) of service.

Keywords

• Virtual network function
• deep belief network
• multi-task learning
• migration