Int J Performability Eng ›› 2020, Vol. 16 ›› Issue (2): 314-324.doi: 10.23940/ijpe.20.02.p14.314324

• Orginal Article • Previous Articles    

An Adaptive Traffic-Aware Migration Algorithm Selection Framework in Live Migration of Multiple Virtual Machines

Yong Cui*(), Liang Zhu, Zengyu Cai, and Ying Hu   

  1. School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450001, China
  • Submitted on ; Revised on ; Accepted on
  • Contact: Yong Cui
  • About author:

    Yong Cui received his Ph.D. from Zhengzhou University. He is a lecturer in the School of Computer and Communication Engineering at Zhengzhou University of Light Industry. His research interests include cloud computing and virtualization technology.

  • Supported by:
    This research is supported by the National Natural Science Foundation of China (No. 61902361) and Fundamental Research Funds for the Henan Province University (No. 17KYYWF0202) .


In IaaS cloud computing platforms, live migration of multiple virtual machines plays a dominant role in the dynamic scheduling, optimization and management of IT resources. Although Pre-copy and Post-copy are the prevalent live migration algorithms for the single virtual machine, which both have pros and cons, only one of them is monotonously adopted in the context of the gang of live migration. This scheme cannot choose the best migration algorithm for each virtual machine according to its business traffic and cause a traffic contention problem. This paper proposes an adaptive traffic-aware live migration algorithm selection framework, which leverages fuzzy clustering method to classify the virtual machines to be migrated according to their business traffics and migrate them with the chosen best-fitting migration algorithms. Experiment results show that the proposed framework can effectively choose the suitable migration algorithms for classified virtual machines and eventually improve the whole live migration performance meanwhile avoiding the degradation of business performance.

Key words: live migration, virtual machine, Pre-copy, Post-copy, cloud computing, performance optimization