Skip to main content

Advertisement

Springer Nature Link
Log in

An improved Lévy based whale optimization algorithm for bandwidth-efficient virtual machine placement in cloud computing environment

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

The consolidation of virtual machine (VM) is the strategy of efficient and intelligent use of cloud datacenters resources. One of the important subproblems of VM consolidation is VM placement problem. The main objective of VM placement problem is to minimize the number of running physical machines or hosts in cloud datacenters. This paper focuses on solving VM placement problem with respect to the available bandwidth which is formulated as variable sized bin packing problem. Moreover, a new bandwidth allocation policy is developed and hybridized with an improved variant of whale optimization algorithm (WOA) called improved Lévy based whale optimization algorithm. Cloudsim toolkit is used in order to test the validity of the proposed algorithm on 25 different data sets that generated randomly and compared with many optimization algorithms including: WOA, first fit, best fit, particle swarm optimization, genetic algorithm, and intelligent tuned harmony search. The obtained results are analyzed by Friedman test which indicates the prosperity of the proposed algorithm for minimizing the number of running physical machine.

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

Access this article

Log in via an institution

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

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

Similar content being viewed by others

References

  1. Qian, L., Luo, Z., Du, Y., Guo, L.: Cloud computing: an overview. In: Jaatun, M.G., Zhao, G., Rong, C. (eds.) Cloud Computing, pp. 626–631. Springer, Berlin (2009)

    Google Scholar 

  2. Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. J Internet Serv. Appl. 1(1), 7–18 (2010)

    Google Scholar 

  3. Rhoton, J.: Cloud computing explained. Recursive Press, London (2013)

    Google Scholar 

  4. Wohl, A.: Software as a Service (SaaS). In: Simon, P. (ed.) The Next Wave of Technologie: Opportunities from Chaos, pp. 97–113. Wiley, Hoboken (2010)

    Google Scholar 

  5. Santana, M.: Infrastructure as a Service (IaaS). In: Vacca, J.R. (ed.) Cloud Computing Security: Foundations and Challenges. CRC Press, Boca Raton (2016)

    Google Scholar 

  6. Beimborn, D., Miletzki, T., Wenzel, S.: Platform as a service (PaaS). Wirtschaftsinformatik 53(5), 371–375 (2011)

    Google Scholar 

  7. Abawajy, J.H.: An efficient adaptive scheduling policy for high-performance computing. Future Gener. Comput. Syst. 25(2), 364–370 (2009)

    Google Scholar 

  8. Borgetto, D., Casanova, H., Da Costa, G., Pierson, J.M.: Energy-aware service allocation. Future Gener. Comput. Syst. 28(4), 769–779 (2012)

    Google Scholar 

  9. Stavrinides, G.L., Karatza, H.D.: Scheduling real-time DAGs in heterogeneous clusters by combining imprecise computations and bin packing techniques for the exploitation of schedule holes. Future Gener. Comput. Syst. 28(6), 977–988 (2012)

    Google Scholar 

  10. Verbelen, T., Stevens, T., De Turck, F., Dhoedt, B.: Graph partitioning algorithms for optimizing software deployment in mobile cloud computing. Future Gener. Comput. Syst. 29(1), 451–459 (2013)

    Google Scholar 

  11. Sheikhalishahi, M., Wallace, R.M., Grandinetti, L., Vazquez-Poletti, J.L., Guerriero, F.: A multi-dimensional job scheduling. Future Gener. Comput. Syst. 54, 123–131 (2016)

    Google Scholar 

  12. Bassem, C., Bestavros, A.: Multi-Capacity bin packing with dependent items and its application to the packing of brokered workloads in virtualized environments. Future Gener. Comput. Syst. 72, 129–144 (2017)

    Google Scholar 

  13. Ye, D., Chen, J.: Non-cooperative games on multidimensional resource allocation. Future Gener. Comput. Syst. 29(5), 1345–1352 (2013)

    Google Scholar 

  14. Kessaci, Y., Melab, N., Talbi, E.G.: A multi-start local search heuristic for an energy efficient VMs assignment on top of the OpenNebula cloud manager. Future Gener. Comput. Syst. 36, 237–256 (2014)

    Google Scholar 

  15. Rao, K.S., Thilagam, P.S.: Heuristics based server consolidation with residual resource defragmentation in cloud data centers. Future Gener. Comput. Syst. 50, 87–98 (2015)

    Google Scholar 

  16. Aroca, J.A., Anta, A.F., Mosteiro, M.A., Thraves, C., Wang, L.: Power-efficient assignment of virtual machines to physical machines. Future Gener. Comput. Syst. 54, 82–94 (2016)

    Google Scholar 

  17. Hallawi, H., Mehnen, J., He, H.: Multi-Capacity Combinatorial Ordering GA in Application to Cloud resources allocation and efficient virtual machines consolidation. Future Gener. Comput. Syst. 69, 1–10 (2017)

    Google Scholar 

  18. Xing, Y., Zhan, Y.: Virtualization and cloud computing. In: Zhang, Y. (ed.) Future Wireless Networks and Information Systems, pp. 305–312. Springer, Berlin (2012)

    Google Scholar 

  19. Vaezi, M., Zhang, Y.: Virtualization and cloud computing. In: Vaezi, M., Zhang, Y. (eds.) Cloud Mobile Networks. Springer, New York (2017)

    Google Scholar 

  20. Lopez-Pires, F., Barán, B.: Virtual machine placement literature review. arXiv preprint, arXiv:1506.01509 (2015)

  21. Challita, S., Paraiso, F., Merle, P.: Study of virtual machine placement optimization in data centers. In: Proceedings of the 7th International Conference on Cloud Computing and Services Science, CLOSER 2017 (2017)

  22. Chaisiri, S., Lee, B.S., Niyato, D.: Optimal virtual machine placement across multiple cloud providers. In: 2009 IEEE Asia-Pacific Services Computing Conference (APSCC 2009), pp. 103–110. IEEE (2009)

  23. Alicherry, M., Lakshman, T.V.: Optimizing data access latencies in cloud systems by intelligent virtual machine placement. In: 2013 Proceedings IEEE INFOCOM, pp. 647–655. IEEE (2013)

  24. Dang, H.T., Hermenier, F.: Higher SLA satisfaction in datacenters with continuous VM placement constraints. In: Proceedings of the 9th Workshop on Hot Topics in Dependable Systems, p. 1. ACM (2013)

  25. Adamuthe, A.C., Pandharpatte, R.M., Thampi, G.T.: Multiobjective virtual machine placement in cloud environment. In: Proceedings of the 2013 International Conference on Cloud & Ubiquitous Computing & Emerging Technologies (CUBE), pp. 8–13. IEEE (2013)

  26. Sampson, J.R.: Adaptation in Natural and Artificial Systems (John H. Holland). SIAM Rev. 18(2), 529–530 (1976)

    Google Scholar 

  27. Srinivas, N., Deb, K.: Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol. Comput. 2(2), 221–248 (1994)

    Google Scholar 

  28. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.A.M.T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(1), 182–197 (2002)

    Google Scholar 

  29. Xu, J., Fortes, J.A.: Multi-objective virtual machine placement in virtualized data center environments. In: Proceedings of the 2010 IEEE/ACM International Conference on Green Computing and Communications & International Conference on Cyber, Physical and Social Computing, pp. 179–188. IEEE Computer Society (2010)

  30. Zadeh, L.A.: Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers by Lotfi A Zadeh, vol. 6. World Scientific, Singapore (1996)

    Google Scholar 

  31. Perumal, B., Murugaiyan, A.: A firefly colony and its fuzzy approach for server consolidation and virtual machine placement in cloud datacenters. Adv. Fuzzy Syst. 2016, 5 (2016)

    MathSciNet  Google Scholar 

  32. Yang, X.S.: Firefly algorithms for multimodal optimization. In: International Symposium on Stochastic Algorithms, pp. 169–178. Springer, Berlin (2009)

  33. Wu, G., Tang, M., Tian, Y.C., Li, W.: Energy-efficient virtual machine placement in data centers by genetic algorithm. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds.) Neural Information Processing, pp. 315–323. Springer, Berlin (2012)

    Google Scholar 

  34. Wu, Y., Tang, M., Fraser, W.: A simulated annealing algorithm for energy efficient virtual machine placement. In: Proceedings of the 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1245–1250. IEEE (2012)

  35. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)

    MathSciNet  Google Scholar 

  36. Gao, Y., Guan, H., Qi, Z., Hou, Y., Liu, L.: A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. J. Comput. Syst. Sci. 79(7), 1230–1242 (2013)

    MathSciNet  Google Scholar 

  37. Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)

    Google Scholar 

  38. Wang, S., Liu, Z., Zheng, Z., Sun, Q., Yang, F.: Particle swarm optimization for energy-aware virtual machine placement optimization in virtualized data centers. In: Proceedings of the 2013 International Conference on Parallel and Distributed Systems (ICPADS), pp. 102–109. IEEE (2013)

  39. Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science (MHS’95), pp. 39–43. IEEE (1995)

  40. Joshi, S., Kaur, S.: Cuckoo search approach for virtual machine consolidation in cloud data centre. In: Proceedings of the 2015 International Conference on Computing, Communication & Automation (ICCCA), pp. 683–686. IEEE (2015)

  41. Ali, H.M., Lee, D.C.: A biogeography-based optimization algorithm for energy efficient virtual machine placement. In: Proceedings of the 2014 IEEE Symposium on Swarm Intelligence (SIS), pp. 1–6. IEEE (2014)

  42. Yang, X.S., Deb, S.: Cuckoo search via Lévy flights. In: World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), pp. 210–214. IEEE (2009)

  43. Simon, D.: Biogeography-based optimization. IEEE Trans. Evol. Comput. 12(5), 702–713 (2008)

    Google Scholar 

  44. Alboaneen, D.A., Tianfield, H., Zhang, Y.: Glowworm swarm optimisation algorithm for virtual machine placement in cloud computing. In: Proceedings of the 2016 International IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), pp. 808–814. IEEE (2016)

  45. Patel, P., Ranabahu, A., Sheth, A.: Service level agreement in cloud computing. In: Cloud Workshops at OOPSLA09, Orlando, FL, 25–29 October (2009)

  46. Krishnanand, K.N., Ghose, D.: Glowworm swarm based optimization algorithm for multimodal functions with collective robotics applications. Multiagent Grid Syst. 2(2), 209–222 (2006)

    Google Scholar 

  47. Friesen, D.K., Langston, M.A.: Variable sized bin packing. SIAM J. Comput. 15(1), 222–230 (1986)

    Google Scholar 

  48. Haouari, M., Serairi, M.: Relaxations and exact solution of the variable sized bin packing problem. Comput. Optim. Appl. 48(1), 345–368 (2011)

    MathSciNet  Google Scholar 

  49. Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)

    Google Scholar 

  50. Fredriksson, L.: A Brief Survey of Lévy Walks: with applications to probe diffusion. Bachelor Dissertation. http://www.divaportal.org/smash/get/diva2:288755/FULLTEXT02.pdf (2010)

  51. Mantegna, R.N.: Fast, accurate algorithm for numerical simulation of Levy stable stochastic processes. Phys. Rev. E 49(4), 4677 (1994)

    Google Scholar 

  52. Yang, X.S., Karamanoglu, M., He, X.: Flower pollination algorithm: a novel approach for multiobjective optimization. Eng. Optim. 46(8), 1222–1237 (2014)

    MathSciNet  Google Scholar 

  53. Devaney, R.: An Introduction to Chaotic Dynamical Systems. Addison-Wesley, Reading (1989). 13046

    Google Scholar 

  54. Qian, B., Wang, L., Rong, H., Wang, W.L., Huang, D.X., Wang, X.: A hybrid differential evolution method for permutation flow-shop scheduling. Int. J. Adv. Manuf. Technol. 38(7–8), 757–777 (2008)

    Google Scholar 

  55. Rhee, W.T., Talagrand, M.: On line bin packing with items of random size. Math. Oper. Res. 18(1), 438–445 (1993)

    MathSciNet  Google Scholar 

  56. Banzhaf, W.: The "molecular" traveling salesman. Biol. Cybern. 64(1), 7–14 (1990)

    Google Scholar 

  57. Michalewicz, Z.: Genetic Algorithms \(+\) Data Structures \(=\) Evolution Programs. Springer, New York (1996)

    Google Scholar 

  58. Grefenstette, J., Gopal, R., Rosmaita, B., Van Gucht, D.: Genetic algorithms for the traveling salesman problem. In: Proceedings of the First International Conference on Genetic Algorithms and their Applications, pp. 160–165 (1985)

  59. Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp. 41(1), 23–50 (2011)

    Google Scholar 

  60. Calheiros, R.N., Ranjan, R., De Rose, C.A., Buyya, R.: Cloudsim: a novel framework for modeling and simulation of cloud computing infrastructures and services. arXiv preprint, arXiv:0903.2525 (2009)

  61. Buyya, R., Ranjan, R., Calheiros, R.N.: Modeling and simulation of scalable Cloud computing environments and the CloudSim toolkit: challenges and opportunities. In: Proceedings of the International Conference on High Performance Computing & Simulation (HPCS’09), pp. 1–11. IEEE (2009)

  62. Johnson, D.S., Demers, A., Ullman, J.D., Garey, M.R., Graham, R.L.: Worst-case performance bounds for simple one-dimensional packing algorithms. SIAM J. Comput. 3(3), 299–325 (1974)

    MathSciNet  Google Scholar 

  63. Yadav, P., Kumar, R., Panda, S.K., Chang, C.S.: An intelligent tuned harmony search algorithm for optimisation. Inf. Sci. 196, 47–72 (2012)

    Google Scholar 

  64. Mirjalili, S., Mirjalili, S.M., Hatamlou, A.: Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput. Appl. 27(1), 495–513 (2016)

    Google Scholar 

  65. Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference, pp. 977–979. Springer, Berlin (2011)

    Google Scholar 

  66. Asogbon, M.G., Samuel, O.W., Omisore, M.O., Awonusi, O.: Enhanced neuro-fuzzy system based on genetic algorithm for medical diagnosis. J. Med. Diagn. Methods 5(205), 2 (2016)

    Google Scholar 

  67. Omisore, M.O., Samuel, O.W., Atajeromavwo, E.J.: A genetic-neuro-fuzzy inferential model for diagnosis of tuberculosis. Appl. Comput. Inform. 13(1), 27–37 (2015)

    Google Scholar 

  68. Samuel, O.W., Omisore, M.O., Ojokoh, B.A., Atajeromavwo, E.J.: Enhanced cloud based model for healthcare delivery organizations in developing countries. Int. J. Comput. Appl. 74(1), (2013)

Download references

Author information

Authors and Affiliations

  1. Department of Operations Research, Faculty of Computers and Informatics, Zagazig University, Sharqiyah, Egypt

    Mohamed Abdel-Basset & Laila Abdle-Fatah

  2. School of Computing Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India

    Arun Kumar Sangaiah

Authors
  1. Mohamed Abdel-Basset
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Laila Abdle-Fatah
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Arun Kumar Sangaiah
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Arun Kumar Sangaiah.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (doc 88 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abdel-Basset, M., Abdle-Fatah, L. & Sangaiah, A.K. An improved Lévy based whale optimization algorithm for bandwidth-efficient virtual machine placement in cloud computing environment. Cluster Comput 22 (Suppl 4), 8319–8334 (2019). https://doi.org/10.1007/s10586-018-1769-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-1769-z

Keywords