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Virtual Machine Placement Using Adam White Shark Optimization Algorithm in Cloud Computing

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Abstract

The increasing demand for virtual machine (VM) request is caused due to the increasing number of users. Hence, the VM placement is considered as a critical task for attaining effective resource handling in cloud data centers (DCs). In general, the VM placement procedure deploys the set of VMs onto the set of physical machines (PMs) depending on specific criteria. In this research, the optimal solution for VM placement is computed by hybrid optimization with fitness parameters. Here, the fitness function is computed by combining several objectives including load, power, placement time and migration cost. In addition, VM placement is based on several system factors such as central processing unit (CPU), memory, and bandwidth, million instructions per second (MIPS) and processing elements. Besides, the hybrid optimization technique devised for performing the VM migration in this research is Adam white shark optimization-based VM placement (AWSO_VMP), which is formulated by modifying the white shark optimization (WSO) with the Adam optimizer. Thus, the performance of AWSO_VMP is assessed using load, power consumption and cost of migration, and the attained values of corresponding metrics are 0.133, 0.225 W and 0.116.

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Data Availability

The labeled datasets used to support the findings of this study can be obtained from the corresponding author upon request.

References

  1. Shigeta S, Yamashima H, Doi T, Kawai T, Fukui K. Design and implementation of a multi-objective optimization mechanism for virtual machine placement in cloud computing data center. In: Lect. Notes Inst. Comput. Sci. Soc. Telecommun. Eng. LNICST, LNICST, vol 112. 2013. p. 21–31. https://doi.org/10.1007/978-3-319-03874-2_3/COVER.

  2. Gharehpasha S, Masdari M, Jafarian A. Virtual machine placement in cloud data centers using a hybrid multi-verse optimization algorithm. Artif Intell Rev. 2021;54(3):2221–57. https://doi.org/10.1007/S10462-020-09903-9/METRICS.

    Article  Google Scholar 

  3. Supreeth S, Patil K, Patil SD, Rohith S. Comparative approach for VM scheduling using modified particle swarm optimization and genetic algorithm in cloud computing. In: IEEE Int. Conf. Data Sci. Inf. Syst. ICDSIS 2022. 2022. https://doi.org/10.1109/ICDSIS55133.2022.9915907.

  4. Patil K. Hybrid genetic algorithm and modified-particle swarm optimization algorithm (GA-MPSO) for predicting scheduling virtual machines in educational cloud platforms. Int J Emerg Technol Learn (iJET). 2022;17(07):208–25. https://doi.org/10.3991/ijet.v17i07.29223.

    Article  Google Scholar 

  5. Masdari M, Nabavi SS, Ahmadi V. An overview of virtual machine placement schemes in cloud computing. J Netw Comput Appl. 2016;66:106–27. https://doi.org/10.1016/J.JNCA.2016.01.011.

    Article  Google Scholar 

  6. Back T, Hammel U, Schwefel HP. Evolutionary computation: comments on the history and current state. IEEE Trans Evol Comput. 1997;1(1):3–17. https://doi.org/10.1109/4235.585888.

    Article  Google Scholar 

  7. Supreeth S, Patil KK. Virtual machine scheduling strategies in cloud computing—a review. Int J Emerg Technol. 2019;10(3):181–8. https://doi.org/10.5281/ZENODO.6144561.

    Article  Google Scholar 

  8. Liang Z, Zhang J, Feng L, Zhu Z. Multi-factorial optimization for large-scale virtual machine placement in cloud computing. 2020. [Online]. https://arxiv.org/abs/2001.06585v2. Accessed 16 July 2023.

  9. Supreeth S, Patil K. VM scheduling for efficient dynamically migrated virtual machines (VMS-EDMVM) in cloud computing environment. KSII Trans Internet Inf Syst. 2022;16(6):1892–912. https://doi.org/10.3837/tiis.2022.06.007.

    Article  Google Scholar 

  10. 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. 2013;79(8):1230–42. https://doi.org/10.1016/J.JCSS.2013.02.004.

    Article  MathSciNet  MATH  Google Scholar 

  11. Kumaraswamy S, Nair MK. Bin packing algorithms for virtual machine placement in cloud computing: a review. Int J Electr Comput Eng. 2019;9(1):512–24. https://doi.org/10.11591/IJECE.V9I1.PP512-524.

    Article  Google Scholar 

  12. Mejahed S, Elshrkawey M. A multi-objective algorithm for virtual machine placement in cloud environments using a hybrid of particle swarm optimization and flower pollination optimization. PeerJ Comput Sci. 2022;8: e834. https://doi.org/10.7717/PEERJ-CS.834/SUPP-1.

    Article  Google Scholar 

  13. Abdel-Basset M, Abdle-Fatah L, Sangaiah AK. An improved Lévy based whale optimization algorithm for bandwidth-efficient virtual machine placement in cloud computing environment. Cluster Comput. 2019;22(4):8319–34. https://doi.org/10.1007/S10586-018-1769-Z/METRICS.

    Article  Google Scholar 

  14. Al-Moalmi A, Luo J, Salah A, Li K. Optimal virtual machine placement based on grey wolf optimization. Electronics. 2019;8(3):283. https://doi.org/10.3390/ELECTRONICS8030283.

    Article  Google Scholar 

  15. **ong AP, Xu CX. Energy efficient multiresource allocation of virtual machine based on PSO in cloud data center. Math Probl Eng. 2014. https://doi.org/10.1155/2014/816518.

    Article  Google Scholar 

  16. Alashaikh AS, Alanazi EA. Incorporating ceteris paribus preferences in multiobjective virtual machine placement. IEEE Access. 2019;7:59984–98. https://doi.org/10.1109/ACCESS.2019.2916090.

    Article  Google Scholar 

  17. Zhao DM, Zhou JT, Li K. An energy-aware algorithm for virtual machine placement in cloud computing. IEEE Access. 2019;7:55659–68. https://doi.org/10.1109/ACCESS.2019.2913175.

    Article  Google Scholar 

  18. Saxena D, Gupta I, Kumar J, Singh AK, Wen X. A secure and multiobjective virtual machine placement framework for cloud data center. IEEE Syst J. 2022;16(2):3163–74. https://doi.org/10.1109/JSYST.2021.3092521.

    Article  Google Scholar 

  19. Gharehpasha S, Masdari M. A discrete chaotic multi-objective SCA-ALO optimization algorithm for an optimal virtual machine placement in cloud data center. J Ambient Intell Humaniz Comput. 2021;12(10):9323–39. https://doi.org/10.1007/S12652-020-02645-0/METRICS.

    Article  Google Scholar 

  20. Fatima A, et al. Virtual machine placement via bin packing in cloud data centers. Electronics. 2018;7(12):389. https://doi.org/10.3390/ELECTRONICS7120389.

    Article  Google Scholar 

  21. Farzai S, Shirvani MH, Rabbani M. Multi-objective communication-aware optimization for virtual machine placement in cloud datacenters. Sustain Comput Inform Syst. 2020;28: 100374. https://doi.org/10.1016/J.SUSCOM.2020.100374.

    Article  Google Scholar 

  22. Alboaneen D, Tianfield H, Zhang Y, Pranggono B. A metaheuristic method for joint task scheduling and virtual machine placement in cloud data centers. Futur Gener Comput Syst. 2021;115:201–12. https://doi.org/10.1016/J.FUTURE.2020.08.036.

    Article  Google Scholar 

  23. Alharbe N, Rakrouki MA, Aljohani A. An improved ant colony algorithm for solving a virtual machine placement problem in a cloud computing environment. IEEE Access. 2022;10:44869–80. https://doi.org/10.1109/ACCESS.2022.3170103.

    Article  Google Scholar 

  24. Hosseini Shirvani M. A hybrid meta-heuristic algorithm for scientific workflow scheduling in heterogeneous distributed computing systems. Eng Appl Artif Intell. 2020;90: 103501. https://doi.org/10.1016/J.ENGAPPAI.2020.103501.

    Article  Google Scholar 

  25. Hosseini Shirvani M. An energy-efficient topology-aware virtual machine placement in Cloud Datacenters: a multi-objective discrete JAYA optimization. Sustain Comput Inform Syst. 2023;38: 100856. https://doi.org/10.1016/J.SUSCOM.2023.100856.

    Article  Google Scholar 

  26. Aghasi A, Jamshidi K, Bohlooli A, Javadi B. A decentralized adaptation of model-free Q-learning for thermal-aware energy-efficient virtual machine placement in cloud data centers. Comput Networks. 2023;224: 109624. https://doi.org/10.1016/J.COMNET.2023.109624.

    Article  Google Scholar 

  27. Ding Z, Tian YC, Wang YG, Zhang WZ, Yu ZG. Accelerated computation of the genetic algorithm for energy-efficient virtual machine placement in data centers. Neural Comput Appl. 2023;35(7):5421–36. https://doi.org/10.1007/S00521-022-07941-8/FIGURES/12.

    Article  Google Scholar 

  28. Sheeba A, Uma Maheswari B. An efficient fault tolerance scheme based enhanced firefly optimization for virtual machine placement in cloud computing. Concurr Comput Pract Exp. 2023;35(7): e7610. https://doi.org/10.1002/CPE.7610.

    Article  Google Scholar 

  29. Gabhane JP, Pathak S, Thakare N. An improved multi-objective eagle algorithm for virtual machine placement in cloud environment. Microsyst Technol. 2023. https://doi.org/10.1007/S00542-023-05422-Z/METRICS.

    Article  Google Scholar 

  30. Mukhija L, Sachdeva R. An effective mechanism for virtual machine placement using cuckoo search. In: 2nd Ed. IEEE Delhi Sect. Own. Conf. DELCON 2023—Proc. 2023. https://doi.org/10.1109/DELCON57910.2023.10127396.

  31. Mehta S, Kaur P, Agarwal P. Improved whale optimization variants for SLA-compliant placement of virtual machines in cloud data centers. Multimed Tools Appl. 2023. https://doi.org/10.1007/S11042-023-15528-1/METRICS.

    Article  Google Scholar 

  32. Shruthi G, Mundada MR, Sowmya BJ, Supreeth S. Mayfly Taylor optimisation-based scheduling algorithm with deep reinforcement learning for dynamic scheduling in fog-cloud computing. Appl Comput Intell Soft Comput. 2022;2022:1–17. https://doi.org/10.1155/2022/2131699.

    Article  Google Scholar 

  33. Shruthi G, Mundada M, Supreeth S. Resource allocation using weighted greedy knapsack based algorithm in an educational fog computing environment. Int J Emerg Technol Learn (iJET). 2022;17(18):261–74. https://doi.org/10.3991/ijet.v17i18.32363.

    Article  Google Scholar 

  34. Supreeth S, Patil K, Patil SD, Rohith S, Vishwanath Y, Prasad KSV. An efficient policy-based scheduling and allocation of virtual machines in cloud computing environment. J Electr Comput Eng. 2022. https://doi.org/10.1155/2022/5889948.

    Article  Google Scholar 

  35. Kingma DP, Ba JL. Adam: a method for stochastic optimization. In: 3rd Int. Conf. Learn. Represent. ICLR 2015—Conf. Track Proc., Dec. 2014. [Online]. https://arxiv.org/abs/1412.6980v9 Accessed 16 July 2023.

  36. Braik M, Hammouri A, Atwan J, Al-Betar MA, Awadallah MA. White shark optimizer: a novel bio-inspired meta-heuristic algorithm for global optimization problems. Knowl Based Syst. 2022;243: 108457. https://doi.org/10.1016/J.KNOSYS.2022.108457.

    Article  Google Scholar 

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Acknowledgements

The authors acknowledge the support from REVA University for the facilities provided to carry out the research.

Funding

No funding received for this research.

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Authors and Affiliations

  1. School of Computer Science and Engineering, REVA University, Bengaluru, India

    S. Supreeth

  2. Department of Electronics and Communication Engineering, S J C Institute of Technology, Chikkaballapur, India

    S. Bhargavi

  3. Healthcare IT Leader, SecureKloud Technologies Inc, Chicago, USA

    Rakesh Margam

  4. Government Engineering College, Hassan, Karnataka, India

    H. Annaiah

  5. Nitte Meenakshi Institute of Technology, Bengaluru, India

    Rajesh Nandalike

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Contributions

SS and SB identified initial problem identification, algorithm write-up, analysis, drafting of the manuscript, and simulation. RM was responsible for the literature survey and helped in the initial review process. AH was responsible for the complexity analysis of the research, evaluation of the research work. RN was responsible for the figures, final formatting and applied for the journal. All the authors worked together to implement and evaluate the integrated system, and approved the final version of the paper.

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Correspondence to S. Supreeth.

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This article is part of the topical collection “Advances in Computational Approaches for Image Processing, Wireless Networks, Cloud Applications and Network Security” guest edited by P. Raviraj, Maode Ma and Roopashree H R.

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Supreeth, S., Bhargavi, S., Margam, R. et al. Virtual Machine Placement Using Adam White Shark Optimization Algorithm in Cloud Computing. SN COMPUT. SCI. 5, 21 (2024). https://doi.org/10.1007/s42979-023-02341-8

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