Advertisement

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

  • 4 Accesses

Abstract

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.

This is a preview of subscription content, log in to check access.

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 99

This is the net price. Taxes to be calculated in checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

References

  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):13–53

  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):375

  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):137–146

  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):7–18

  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):430–447

  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):236–262

  7. 7.

    Kumar R, Charu S (2015) An importance of using virtualization technology in cloud computing. Global Journal of Computers & Technology 1(2)

  8. 8.

    Malhotra L, Agarwal D, Jaiswal A (2014) Virtualization in cloud computing. International Journal of Computer Science & Mobile Computing 3(8)

  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 Computing

  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 Systems

  11. 11.

    Mishra M, Das A, Kulkarni P, Sahoo A (2012) Dynamic resource management using virtual machine migrations. IEEE Commun Mag 50(9):34–40

  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 70:931–938

  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):1853–1865

  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 S0167739X1630053X

  15. 15.

    Zhu J, Wang J, Zhang Y, Jiang Y (2018) Virtual machine migration method based on load cognition. Soft Computing (5), 1–10

  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):755–768

  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 268–273

  18. 18.

    Shayeji MHA, Samrajesh M (2012) An energy-aware virtual machine migration algorithm. In: International Conference on Advances in Computing & Communications

  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):327–345

  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 58:3–14

  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):39–41

  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):78–82

  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 618–624

  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 27–30

  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 1–6

Download references

Author information

Correspondence to Yi Jiang.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This work was supported in part by the National Natural Science Foundation of China under grant nos. 61872313; the Key Research Projects in Education Informatization in Jiangsu Province under grant 20180012; by the Postgraduate Research and Practice Innovation Program of Jiangsu Province under grant KYCX18 2366; and by Yangzhou Science and Technology under grant YZ2018209, YZ2019133; and by Yangzhou University Jiangdu High-end Equipment Engineering Technology Research Institute Open Project under grant YDJD201707.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Jiang, Y., Wang, J., Shi, J. et al. Network-aware Virtual Machine Migration Based on Gene Aggregation Genetic Algorithm. Mobile Netw Appl (2020) doi:10.1007/s11036-019-01376-7

Download citation

Keywords

  • Virtualization
  • Virtual machine migration
  • Gene aggregation
  • Genetic algorithm