Advertisement

Efficient Evolutionary Approach for Virtual Machine Placement in Cloud Data Center

  • Geetika Mudali
  • K. Hemant Kumar ReddyEmail author
  • Diptendu Sinha Roy
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1059)

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.

Keywords

Virtual machine placement PSO Distributed PSO Energy efficient Cloud computing Evolutionary approach 

References

  1. 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 1–10Google Scholar
  2. 2.
    Lawey AQ, El-Gorashi TEH, Elmirghani JMH (2014) Distributed energy efficient clouds over core networks. J Lightw Technol 32(7):1261–1281CrossRefGoogle Scholar
  3. 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 117–118Google Scholar
  4. 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.2016.2597826CrossRefGoogle Scholar
  5. 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 112–119Google Scholar
  6. 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 1419–1420Google Scholar
  7. 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):215–228CrossRefGoogle Scholar
  8. 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. 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 13Google Scholar
  10. 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):131–141CrossRefGoogle Scholar
  11. 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):68–73CrossRefGoogle Scholar
  12. 12.
    Reddy KHK, Mudali G, Roy DS (2017) A novel coordinated resource provisioning approach for cooperative cloud market. J Cloud Comput 6(1):8CrossRefGoogle Scholar
  13. 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. 14.
    Bui TN, Moon BR (1996) Genetic algorithm and graph partitioning. IEEE Trans Comput 45(7):841–855MathSciNetCrossRefGoogle Scholar
  15. 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 41–48Google Scholar
  16. 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):445–463Google Scholar
  17. 17.
    Ajiro Y, Tanaka A (2007) Improving packing algorithms for server consolidation. In: Proceedings of the international conference Computer Measurement Group, pp 399–406Google Scholar
  18. 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 362–369Google Scholar
  19. 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):474–476CrossRefGoogle Scholar
  20. 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 1942–1948Google Scholar
  21. 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 4104–4108Google Scholar
  22. 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 1582–1587Google Scholar
  23. 23.
    Capko D et al (2009) PSO algorithm for graph partitioning. 17th Telecommunication Forum 2009, BelgradeGoogle Scholar
  24. 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):292–307Google Scholar
  25. 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

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Geetika Mudali
    • 1
  • K. Hemant Kumar Reddy
    • 1
    Email author
  • Diptendu Sinha Roy
    • 2
  1. 1.Department of Computer Science and EngineeringNational Institute of Science and TechnologyBerhampurIndia
  2. 2.Department of Computer Science and EngineeringNational Institute of TechnologyShillongIndia

Personalised recommendations