Skip to main content

Advertisement

SpringerLink
Log in

Improved whale optimization variants for SLA-compliant placement of virtual machines in cloud data centers

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Infrastructure as a Service (IaaS) model of cloud computing provides resources, such as storage and computation power from cloud-based physical machines to users in the form of virtual machines (VM). This can be cost-efficient for users as well as cloud service providers if the physical machines and related resources are optimally utilized. At the same time, it is also imperative that energy consumption by physical machines in cloud data centers be minimized. A solution to both these challenges can be found by efficient mapping of VMs onto physical machines. This is known as the VM placement problem and the paper aims to find an optimal mapping of VMs to PMs with two objectives. Firstly, the cloud service provider’s cost is reduced by increasing the resource utilization and secondly, the energy consumption is reduced by decreasing the number of PMs in use at a time. In this work, two variants of whale optimization algorithm (WOA) are proposed for efficiently placing VMs on the physical machines. In the first variant exploitation phase of WOA is improved via simulated annealing (SWOA). In the second variant Lévy (L) distribution is incorporated to improve the exploration phase of WOA (LWOA). Performance of algorithms is assessed on varying workloads under deadline constraints. Experiments are performed on light, medium, heavy and very heavy workloads. Results demonstrate that the proposed variant SWOA provide efficient solutions on light workload while LWOA gives efficient solution on other workloads for the VM placement problem. These algorithms also converge faster as compared to its contemporary counterparts thereby reducing the computational complexity of the problem.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

References

  1. Ahmad RW, Gani A, Hamid SHA, Shiraz M, Yousafzai A, Xia F (2015) A survey on virtual machine migration and server consolidation frame-works for cloud data centers. J Netw Comput Appl 52:11–25

    Article  Google Scholar 

  2. Alharbi F, Tian YC, Tang M, Zhang WZ, Peng C, Fei M (2019) An ant colony system for energy-efficient dynamic virtual machine placement indata centers. Expert Syst Appl 120:228–238

    Article  Google Scholar 

  3. Ashraf A, Porres I (2018) Multi-objective dynamic virtual machine consolidation in the cloud using ant colony system. Int J Parallel Emergent Distrib Syst 33(1):103–120

    Article  Google Scholar 

  4. Azizi S, Zandsalimi M, Li D (2020) An energy-efficient algorithm for virtual machine placement optimization in cloud data centers. Cluster Comput 23:3421–3434. https://doi.org/10.1007/s10586-020-03096-0

    Article  Google Scholar 

  5. Brazdil PB, Soares C (2000) A comparison of ranking methods for classification algorithm selection. European conference on machine learning. Springer, Berlin

    Google Scholar 

  6. Buyya R, Beloglazov A, Abawajy J (2010) Energy-efficient management of data center resources for cloud computing: a vision, architectural elements, and open challenges. arXiv preprint arXiv:1006.0308

  7. Caviglione L, Gaggero M, Paolucci M et al (2021) Deep reinforcement learning for multi-objective placement of virtual machines in cloud datacenters. Soft Comput. https://doi.org/10.1007/s00500-020-05462-x

  8. Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, New York

    Google Scholar 

  9. Dignan L (2019) Top cloud providers 2019: AWS, Microsoft Azure, Google Cloud; IBM makes hybrid move; Salesforce dominates SaaS, https://www.zdnet.com/article/top-cloud-providers-2019-aws-microsoft-azure-google-cloud-ibm-makes-hybrid-move-salesforce-dominates-saas/. Accessed on 25 Sept 2020

  10. Ding W, Gu C, Luo F et al (2018) DFA-VMP: an efficient and secure virtual machine placement strategy under cloud environment. Peer-to-Peer Netw Appl 11(2):318–333

    Article  Google Scholar 

  11. El Motaki S, Yahyaouy A, Gualous H, Sabor J (2019) Comparative study between exact and metaheuristic approaches for virtual machine placement process as knapsack problem. J Supercomput. https://doi.org/10.1007/s11227-019-02847-0

  12. Gao Y, Guan H, Qi Z, Hou Y, Liu L (2013) A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. J Comput Syst Sci 79(8):1230–1242

    Article  MathSciNet  Google Scholar 

  13. Guo L, He Z, Zhao S, Zhang N, Wang J, Jiang C (2012) Multi-objective optimization for data placement strategy in cloud computing. In: International conference on information computing and applications. Springer, Berlin, pp 119–126

    Chapter  Google Scholar 

  14. Jaggi P, Mehta S (2016) Resource provisioning and work flow scheduling in clouds using augmented shuffled frog leaping algorithm. J Parallel Distrib Comput 101:41–50. https://doi.org/10.1016/j.jpdc.2016.11.003

    Article  Google Scholar 

  15. Kaur P, Mehta S (2019) Efficient computation offloading using grey wolf optimization algorithm. AIP Conf Proc 2061:020011. https://doi.org/10.1063/1.5086633

    Article  Google Scholar 

  16. Kaur A, Gupta P, Singh M, Nayyar A (2019) Data placement in era of cloud computing: a survey, taxonomy and open research issues. Scalable Comput 20(2):377–398

    Google Scholar 

  17. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN'95-international conference on neural networks, vol 4. IEEE, pp 1942–1948

    Chapter  Google Scholar 

  18. Li XK, Gu CH, Yang ZP, Chang YH (2015) December. Virtual machine placement strategy based on discrete firefly algorithm in cloud environments. In: Wavelet active media technology and information processing (ICCWAMTIP), 2015 12th international computer conference on. IEEE, pp 61–66

    Google Scholar 

  19. Li Z, Yan C, Yu L, Yu X (2018) Energy-aware and multi-resource overload probability constraint-based virtual machine dynamic consolidation method. Futur Gener Comput Syst 80:139–156

    Article  Google Scholar 

  20. Ling Y, Zhou Y, Luo Q (2017) Lévy flight trajectory-based whale optimization algorithm for global optimization. IEEE Access 5:6168–6186. https://doi.org/10.1109/ACCESS.2017.2695498

    Article  Google Scholar 

  21. Liu C, Shen C, Li S, Wang S (2014) A new evolutionary multi-objective algorithm to virtual machine placement in virtualized data center. In: 2014 IEEE 5th international conference on software engineering and service science. IEEE, pp 272–275

    Chapter  Google Scholar 

  22. López-Pires F, Barán B (2013) Multi-objective virtual machine placement with service level agreement: a memetic algorithm approach. In: Proceedings of the 2013 IEEE/ACM 6th international conference on utility and cloud computing. IEEE Computer Society, pp 203–210

  23. López-Pires F, Barán B (2017) Many-objective optimization for virtual machine placement in cloud computing. In: Research advances in cloud computing. Springer, Singapore, pp 291–326

    Chapter  Google Scholar 

  24. Luo J, Song W, Yin L (2018) Reliable virtual machine placement based on multi-objective optimization with traffic-aware algorithm in indus- trial cloud. IEEE Access 6:23043–23052

    Article  Google Scholar 

  25. Mafarja M, Mirjalili S (2017) Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing. https://doi.org/10.1016/j.neucom.2017.04.053

  26. Malekloo M, Kara N (2014) Multi-objective ACO virtual machine placement in cloud computing environments. In: 2014 IEEE Globecom Workshops (GC Wkshps). IEEE, pp 112–116

    Chapter  Google Scholar 

  27. Malekloo MH, Kara N, El Barachi M (2018) An energy efficient and SLA compliant approach for resource allocation and consolidation in cloud computing environments. Sustain Comput 17:9–24

    Google Scholar 

  28. Mehta S, Kaur P (2019) Efficient computation offloading in Mobile cloud computing with nature-inspired algorithms. Int J Comput Intell Appl 18:1950023. https://doi.org/10.1142/S1469026819500238

    Article  Google Scholar 

  29. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008

    Article  Google Scholar 

  30. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  31. Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191

    Article  Google Scholar 

  32. Mohammed HM, Umar SU, Rashid TA (2019) A systematic and meta-analysis survey of whale optimization algorithm. Computational intelligence and neuroscience, 2019

  33. Nastic S, Morichetta A, Pusztai T, Dustdar S, Ding X, Vij D, Xiong Y (2020) SLOC: service level objectives for next generation cloud computing. IEEE Internet Comput 24(3):39–50

    Article  Google Scholar 

  34. Pham NMN, Le VS (2017) Applying ant colony system algorithm in multi-objective resource allocation for virtual services. J Inf Telecommun 1(4):319–333

    Google Scholar 

  35. Qin Y, Wang H, Yi S, Li X, Zhai L (2020) Virtual machine placement based on multi-objective reinforcement learning. Appl Intell 50:2370–2383

    Article  Google Scholar 

  36. Ramezani F, Naderpour M, Lu J (2016) A multi-objective optimization model for virtual machine mapping in cloud data centres. In: Fuzzy systems (FUZZ-IEEE), 2016 IEEE international conference on, pp 1259–1265

    Chapter  Google Scholar 

  37. Rana N, Shafie AL, Abdulhamid S’i, Chiroma H (2020) Whale optimization algorithm: a systematic review of contemporary applications, modifications and developments. Neural Comput & Applic. https://doi.org/10.1007/s00521-020-04849-z

  38. Salgotra R, Singh U, Saha S (2019) On some improved versions of whale optimization algorithm. Arab J Sci Eng 44(11):9653–9691

    Article  Google Scholar 

  39. Simon D (2013) Evolutionary optimization algorithms. Wiley, Hoboken

    Google Scholar 

  40. Sun G, Li Y, Hongfang Y, Vasilakos AV, Xiaojiang D, Guizani M (2019) Energy-efficient and traffic-aware service function chaining orchestration in multi-domain networks. Futur Gener Comput Syst 91:347–360. https://doi.org/10.1016/j.future.2018.09.037

    Article  Google Scholar 

  41. Tan M, Chi C, Zhang J, Zhao S, Li G, Lü S (2017) An energy-aware virtual machine placement algorithm in cloud data center. In: Proceedings of the 2nd international conference on intelligent information processing, pp 1–9

  42. Varasteh A, Goudarzi M (2017) Server consolidation techniques in virtualized data centers: a survey. IEEE Syst J 11(2):772–783

    Article  Google Scholar 

  43. Wang S, Gu H, Wu G (2013) A new approach to multi-objective virtual machine placement in virtualized data center. In: 2013 IEEE eighth international conference on networking, architecture and storage. IEEE, pp 331–335

    Chapter  Google Scholar 

  44. Wang X, Wang Y, Cui Y (2014) A new multi-objective bi-level programming model for energy and locality aware multi-job scheduling in cloud computing. Futur Gener Comput Syst 36:91–101

    Article  Google Scholar 

  45. Xu J, Fortes JA (2010) Multi-objective virtual machine placement in virtualized data center environments. In: 2010 IEEE/ACM int'l conference on green computing and communications & int'l conference on cyber, physical and social computing. IEEE, pp 179–188

    Chapter  Google Scholar 

  46. Xu B, Peng Z, Xiao F, Gates AM, Yu JP (2015) Dynamic deployment of virtual machines in cloud computing using multi-objective optimization. Soft Comput 19(8):2265–2273

    Article  Google Scholar 

  47. Zhang L, Wang Y, Zhu L, Ji W (2016) Towards energy efficient cloud: an optimized ant colony model for virtual machine placement. J Commun Inf Netw 1(4):116–132

    Article  Google Scholar 

  48. Zheng Q, Li R, Li X, Wu J (2015) A multi-objective biogeography-based optimization for virtual machine placement. In: Cluster, cloud and grid computing (CCGrid), 2015 15th IEEE/ACM international symposium on. IEEE, pp 687–696

    Google Scholar 

  49. Zheng Q, Li R, Li X et al (2016) Virtual machine consolidated placement based on multi-objective biogeography-based optimization. Futur Gener Comput Syst 54:95–122

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Jaypee Institute of Information Technology, Noida, India

    Shikha Mehta, Parmeet Kaur & Parul Agarwal

Authors
  1. Shikha Mehta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Parmeet Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Parul Agarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Parul Agarwal.

Ethics declarations

Conflict of interest

All authors declare that he/she has no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Additional information

Publisher’s note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mehta, S., Kaur, P. & Agarwal, P. Improved whale optimization variants for SLA-compliant placement of virtual machines in cloud data centers. Multimed Tools Appl 83, 149–171 (2024). https://doi.org/10.1007/s11042-023-15528-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-15528-1

Keywords

Search

Navigation