Network-aware Virtual Machine Migration Based on Gene Aggregation Genetic Algorithm

  • Yi JiangEmail author
  • Jinjin Wang
  • Jieke Shi
  • Junwu Zhu
  • Ling Teng


As a key technology of cloud computing, virtualization technology enables multiple virtual machines (VMs) to run on a host to meet the operational needs and environmental requirements of different applications, improving the efficiency of the host. However, the resource of the hosts is limited. When the VMs runs too many tasks, the host will be overloaded and exception occurs. Regarding the issue above, this paper considers the communication cost of virtual machine (VM) migration and proposes a VM Migration Algorithm based on Gene Aggregation Genetic Algorithm (VMM-GAGA). VMM-GAGA mainly solves the problem of allocation between VMs which to be migrated and underutilized hosts. In VMM-GAGA, a novel genetic coding method based on gene aggregation algorithm is proposed. The algorithm performs gene aggregation operations on VMs that have more communication and meet the conditions, which effectively reduces the number of genes in the chromosome., Experiments show that compared with the traditional genetic algorithm, VMM-GAGA reduces search time and communication costs.


Virtualization Virtual machine migration Gene aggregation Genetic algorithm 



  1. 1.
    Yang C, Huang Q, Li Z, Kai L, Fei H (2017) Big data and cloud computing: innovation opportunities and challenges. Int J Digital Earth 10(1):13–53CrossRefGoogle Scholar
  2. 2.
    Rings T, Caryer G, Gallop J, Grabowski J, Kovacikova T, Schulz S, Stokes-Rees I (2009) Grid and cloud computing: opportunities for integration with the next generation network. Journal of Grid Computing 7 (3):375CrossRefGoogle Scholar
  3. 3.
    Wang L, Von Laszewski G, Younge A, He X, Kunze M, Tao J, Fu C (2010) Cloud computing: a perspective study. N Gener Comput 28(2):137–146CrossRefGoogle Scholar
  4. 4.
    Zhang Q, Cheng L, Boutaba R (2010) Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1):7–18CrossRefGoogle Scholar
  5. 5.
    Al-Dhuraibi Y, Paraiso F, Djarallah N, Merle P (2017) Elasticity in cloud computing: state of the art and research challenges. IEEE Trans Serv Comput 11(2):430–447CrossRefGoogle Scholar
  6. 6.
    Mijumbi R, Serrat J, Gorricho JL, Bouten N, De Turck F, Boutaba R (2015) Network function virtualization: State-of-the-art and research challenges. IEEE Commun Surv Tutorials 18(1):236–262CrossRefGoogle Scholar
  7. 7.
    Kumar R, Charu S (2015) An importance of using virtualization technology in cloud computing. Global Journal of Computers & Technology 1(2)Google Scholar
  8. 8.
    Malhotra L, Agarwal D, Jaiswal A (2014) Virtualization in cloud computing. International Journal of Computer Science & Mobile Computing 3(8)Google Scholar
  9. 9.
    Younge AJ, Henschel R, Brown JT, Laszewski GV, Qiu J, Fox GC (2011) Analysis of virtualization technologies for high performance computing environments. In: IEEE International Conference on Cloud ComputingGoogle Scholar
  10. 10.
    Razali RAM, Rahman RA, Zaini N, Samad M (2014) Virtual machine migration implementation in load balancing for cloud computing. In: International Conference on Intelligent & Advanced SystemsGoogle Scholar
  11. 11.
    Mishra M, Das A, Kulkarni P, Sahoo A (2012) Dynamic resource management using virtual machine migrations. IEEE Commun Mag 50(9):34–40CrossRefGoogle Scholar
  12. 12.
    Gao H, Fu Z, Pun CM, et al. (2018) A multi-level thresholding image segmentation based on an improved artificial bee colony algorithm[J]. Comput Electr Eng 70:931–938CrossRefGoogle Scholar
  13. 13.
    Gao H, Shi Y, Pun CM, et al. (2018) An improved artificial bee colony algorithm with its application[J]. IEEE Trans Ind Inf 15(4):1853–1865CrossRefGoogle Scholar
  14. 14.
    Zhang W, Han S, Hui H, Chen H (2016) Network-aware virtual machine migration in an overcommitted cloud. Future Generation Computer Systems p S0167739X1630053XGoogle Scholar
  15. 15.
    Zhu J, Wang J, Zhang Y, Jiang Y (2018) Virtual machine migration method based on load cognition. Soft Computing (5), 1–10Google Scholar
  16. 16.
    Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Futur Gener Comput Syst 28(5):755–768CrossRefGoogle Scholar
  17. 17.
    Reguri VR, Kogatam S, Moh M (2016) Energy efficient traffic-aware virtual machine migration in green cloud data centers. In: 2016 IEEE 2Nd International Conference on Big Data Security on Cloud (bigdatasecurity), IEEE International Conference on High Performance and Smart Computing (HPSC), and IEEE International Conference on Intelligent Data and Security (IDS), IEEE, pp 268–273Google Scholar
  18. 18.
    Shayeji MHA, Samrajesh M (2012) An energy-aware virtual machine migration algorithm. In: International Conference on Advances in Computing & CommunicationsGoogle Scholar
  19. 19.
    Kansal NJ, Chana I (2016) Energy-aware virtual machine migration for cloud computing - a firefly optimization approach. Journal of Grid Computing 14(2):327–345CrossRefGoogle Scholar
  20. 20.
    Wang X, Du Z, Chen Y, Yang M (2015) A green-aware virtual machine migration strategy for sustainable datacenter powered by renewable energy. Simul Model Pract Theory 58:3–14CrossRefGoogle Scholar
  21. 21.
    Shi W, Liu Z (2018) Energy-saving scheduling algorithm for cloud computing center based on virtual machine migration. Computer and Digital Engineering 46(1):39–41Google Scholar
  22. 22.
    Zhao D, Shen S, Wu ZY (2018) An online migration solution for virtual machines for energy saving. Computer Technology and Development 28(2):78–82Google Scholar
  23. 23.
    Dong J, Jin X, Wang H, Li Y, Zhang P, Cheng S (2013) Energy-saving virtual machine placement in cloud data centers. In: 2013 13Th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, IEEE, pp 618–624Google Scholar
  24. 24.
    Wu X, Zeng Y, Lin G (2017) An energy efficient vm migration algorithm in data centers. In: 2017 16Th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES), IEEE, pp 27–30Google Scholar
  25. 25.
    Bharathi PD, Prakash P, Kiran MVK (2017) Energy efficient strategy for task allocation and vm placement in cloud environment. In: 2017 Innovations in Power and Advanced Computing Technologies (i-PACT), IEEE, pp 1–6Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  1. 1.College of Information EngineeringYangzhou UniversityYangzhouChina
  2. 2.School of Information EngineeringYancheng Teachers CollegeYanchengChina

Personalised recommendations