A Random Key Genetic Algorithm for Live Migration of Multiple Virtual Machines in Data Centers

  • Tusher Kumer Sarker
  • Maolin Tang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8835)


Live migration of multiple Virtual Machines (VMs) has become an integral management activity in data centers for power saving, load balancing and system maintenance. While state-of-the-art live migration techniques focus on the improvement of migration performance of an independent single VM, only a little has been investigated to the case of live migration of multiple interacting VMs. Live migration is mostly influenced by the network bandwidth and arbitrarily migrating a VM which has data inter-dependencies with other VMs may increase the bandwidth consumption and adversely affect the performances of subsequent migrations. In this paper, we propose a Random Key Genetic Algorithm (RKGA) that efficiently schedules the migration of a given set of VMs accounting both inter-VM dependency and data center communication network. The experimental results show that the RKGA can schedule the migration of multiple VMs with significantly shorter total migration time and total downtime compared to a heuristic algorithm.


Live migration virtual machine migration time downtime random key genetic algorithm 


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  1. 1.
    Liu, H., Jin, H., Liao, X., Hu, L., Yu, C.: Live migration of virtual machine based on full system trace and replay. In: Proceedings of the 18th ACM International Symposium on High Performance Distributed Computing (HPDC), pp. 101–110 (2009)Google Scholar
  2. 2.
    Akoush, S., Sohan, R., Rice, A., Moore, A.W., Hopper, A.: Predicting the performance of virtual machine migration. In: The 18th Annual IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS), Florida, USA, pp. 37–46 (2010)Google Scholar
  3. 3.
    Ye, K., Jiang, X., Ma, R., Yan, F.: VC-Migration: Live Migration of Virtual Clusters in the Cloud. In: 13th ACM/IEEE International Conference on Grid Computing (GRID), Beijing, China, pp. 209–218 (2012)Google Scholar
  4. 4.
    Deshpande, U., Wang, X., Gopalan, K.: Live gang migration of virtual machines. In: Proceedings of the 20th International Symposium on High Performance Distributed Computing (HPDC), San Jose, California, pp. 135–146 (2011)Google Scholar
  5. 5.
    Sarker, T.K., Tang, M.: Performance-driven live migration of multiple virtual machines in datacenters. In: Proceedings of the 2013 IEEE International Conference on Granular Computing (GrC), Beijing, China, pp. 253–258 (2013)Google Scholar
  6. 6.
    Mann, V., Gupta, A., Dutta, P., Vishnoi, A., Bhattacharya, P., Poddar, R., Iyer, A.: Remedy: Network-aware steady state VM management for data centers. In: Bestak, R., Kencl, L., Li, L.E., Widmer, J., Yin, H. (eds.) NETWORKING 2012, Part I. LNCS, vol. 7289, pp. 190–204. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  7. 7.
    Bean, J.C.: Genetic algorithms and random keys for sequencing and optimization. ORSA Journal on Computing 6, 154–160 (1994)CrossRefzbMATHGoogle Scholar
  8. 8.
    Al-Fares, M., Loukissas, A., Vahdat, A.: A scalable, commodity data center network architecture. In: Proceedings of the ACM SIGCOMM 2008 conference on Data communication (SIGCOMM), Seattle, Washington, USA, pp. 63–74 (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Tusher Kumer Sarker
    • 1
  • Maolin Tang
    • 1
  1. 1.School of Electrical Engineering and Computer ScienceQueensland University of TechnologyBrisbaneAustralia

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