Skip to main content
SpringerLink
Log in

Efficient Evolutionary Approach for Virtual Machine Placement in Cloud Data Center

  • Conference paper
  • First Online:
International Conference on Innovative Computing and Communications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1059))

  • 756 Accesses

Abstract

Administering energy and resource management are two vital managing components of cloud data centers. From last two decades, most of cloud data centers (CDC) are suffering from these two; the former has become a serious issue nowadays. In this paper, we focused on effective virtual machine placement (VMP). Evolutionary approach is applied to place the virtual machine in an effective way which properly utilizes the underutilized resources and reduced the active physical servers. After experiencing the performance of particle swam optimization (PSO) algorithm for combinatorial problems, a distributed PSO approach is modeled to minimize energy consumption of CDCs. The proposed PSO and DPSO algorithms are applied on VMP over large distributed cloud data centers. Experimental results of PSO and distributed PSO algorithms are presented. The model is applied with variety of placement problems with varying data center network topology. The performance of the model outperforms the traditional heuristic and several optimizations approaches.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Foster Y, Zhao I, Raicu, Lu SY (2008) Cloud computing and grid computing 360-degree compared. In: Proceedings of the IEEE grid computing environments workshop, Austin, TX, pp 1–10

    Google Scholar 

  2. Lawey AQ, El-Gorashi TEH, Elmirghani JMH (2014) Distributed energy efficient clouds over core networks. J Lightw Technol 32(7):1261–1281

    Article  Google Scholar 

  3. Liu X-F, Zhan Z-H, Lin J-H, Zhang J (2016) Parallel differential evolution based on distributed cloud computing resources for power electronic circuit optimization. In: Proceedings of the genetic and evolutionary computation conference, Denver, CO, pp 117–118

    Google Scholar 

  4. Zhan ZH et al (2016) Cloudde: a heterogeneous differential evolution algorithm and its distributed cloud version. IEEE Trans Parallel Distrib Syst. https://doi.org/10.1109/tpds.2016.2597826

    Article  Google Scholar 

  5. Chen Z-G et al (2015) Deadline constrained cloud computing resources scheduling through an ant colony system approach. In: Proceeding of the international conference on cloud computing research and innovation, Singapore, pp 112–119

    Google Scholar 

  6. Li H-H, Chen Z-G, Zhan Z-H, Du K-J, Zhang J (2015) Renumber coevolutionary multiswarm particle swarm optimization for multi-objective workflow scheduling on cloud computing environment. In: Proceedings of the genetic and evolutionary computation conference, Madrid, Spain, pp 1419–1420

    Google Scholar 

  7. Mastroianni C, Meo M, Papuzzo G (2013) Probabilistic consolidation of virtual machines in self-organizing cloud data centers. IEEE Trans Cloud Comput 1(2):215–228

    Article  Google Scholar 

  8. Greenpeace (2010) Make it green: cloud computing and its contribution to climate change. Greenpeace International. [Online]. Available http://www.thegreenitreview.com/2010/04/greenpeacereports-on-climate-impact-of.html

  9. Reddy K, Mudali G, Roy DS (2016, March) Energy aware Heuristic scheduling of variable class constraint resources in cloud data centres. In: Proceedings of the 2nd international conference on information and communication technology for competitive strategies. ACM, p 13

    Google Scholar 

  10. Dasgupta G, Sharma A, Verma A, Neogi A, Kothari R (2011) Workload management for power efficiency in virtualized data centers. Commun ACM 54(7):131–141

    Article  Google Scholar 

  11. Greenberg A, Hamilton J, Maltz DA, Patel P (2009) The cost of a cloud: research problems in data center networks. ACM SIGCOMM Comput Commun Rev 39(1):68–73

    Article  Google Scholar 

  12. Reddy KHK, Mudali G, Roy DS (2017) A novel coordinated resource provisioning approach for cooperative cloud market. J Cloud Comput 6(1):8

    Article  Google Scholar 

  13. Mishra J, Sheetlani J, Reddy KHK, Data center network energy consumption minimization: a hierarchical FAT-tree approach. Inter J Inf Technol, 1–13

    Google Scholar 

  14. Bui TN, Moon BR (1996) Genetic algorithm and graph partitioning. IEEE Trans Comput 45(7):841–855

    Article  MathSciNet  Google Scholar 

  15. Liu X-F, Zhan Z-H, Du K-J, Chen W-N (2014) Energy aware virtual machine placement scheduling in cloud computing based on ant colony optimization approach. In: Proceedings of the ACM genetic evolutionary computation conference, Vancouver, BC, pp 41–48

    Google Scholar 

  16. Zhan Z-H et al (2013) Multiple populations for multiple objectives: a coevolutionary technique for solving multiobjective optimization problems. IEEE Trans Cybern 43(2):445–463

    Google Scholar 

  17. Ajiro Y, Tanaka A (2007) Improving packing algorithms for server consolidation. In: Proceedings of the international conference Computer Measurement Group, pp 399–406

    Google Scholar 

  18. Wilcox D, McNabb A, Seppi K (2011) Solving virtual machine packing with a reordering grouping genetic algorithm. In: Proceedings of the IEEE congress of evolutionary computation, New Orleans, LA, pp 362–369

    Google Scholar 

  19. Suseela BBJ, Jeyakrishnan V (2014) A multi-objective hybrid ACOPSO optimization algorithm for virtual machine placement in cloud computing. Int J Res Eng Technol 3(4):474–476

    Article  Google Scholar 

  20. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the 1995 IEEE international conference on neural networks (ICNN), vol 4. IEEE Service Center, Piscataway, New Jersey, pp 1942–1948

    Google Scholar 

  21. Kennedy J, Eberhart R (1997) A discrete binary version of the particle swarm algorithm. In: Proceedings of the 1997 IEEE international conference on systems, man, and cybernetics, vol 5. IEEE Service Center, Piscataway, New Jersey, pp 4104–4108

    Google Scholar 

  22. Laskari E et al (2002) Particle swarm optimization for integer programming. In: Proceedings of the IEEE congress on evolutionary computation, vol 2. Honolulu, Hawaii, pp 1582–1587

    Google Scholar 

  23. Capko D et al (2009) PSO algorithm for graph partitioning. 17th Telecommunication Forum 2009, Belgrade

    Google Scholar 

  24. Laguna-Sánchez GA et al (2009) Comparative study of parallel variants for a particle swarm optimization algorithm implemented on a multithreading GPU. J Appl Res Technol 7(3):292–307

    Google Scholar 

  25. Reddy KHK, Roy DS (2012, March) A hierarchical load balancing algorithm for efficient job scheduling in a computational grid testbed. In: IEEE 1st international conference on recent advances in information technology (RAIT), pp 363–368

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Science and Technology, Berhampur, India

    Geetika Mudali & K. Hemant Kumar Reddy

  2. Department of Computer Science and Engineering, National Institute of Technology, Shillong, Meghalaya, India

    Diptendu Sinha Roy

Authors
  1. Geetika Mudali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. Hemant Kumar Reddy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Diptendu Sinha Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. Hemant Kumar Reddy .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, Maharaja Agrasen Institute of Technology, Delhi, India

    Ashish Khanna

  2. Computer Science and Engineering, Maharaja Agrasen Institute of Technology, Delhi, India

    Deepak Gupta

  3. Department of Information Technology, RCC Institute of Information Technology, Kolkata, West Bengal, India

    Siddhartha Bhattacharyya

  4. Department of Computer Science, VÅ B - Technical University of Ostrava, Ostrava-Poruba, Czech Republic

    Vaclav Snasel

  5. Department of Computer Science, VÅ B - Technical University of Ostrava, Ostrava-Poruba, Czech Republic

    Jan Platos

  6. Faculty of Computers and Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mudali, G., Reddy, K.H.K., Roy, D.S. (2020). Efficient Evolutionary Approach for Virtual Machine Placement in Cloud Data Center. In: Khanna, A., Gupta, D., Bhattacharyya, S., Snasel, V., Platos, J., Hassanien, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1059. Springer, Singapore. https://doi.org/10.1007/978-981-15-0324-5_21

Download citation

Publish with us

Policies and ethics

Search

Navigation