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.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Price excludes VAT (USA)
Tax calculation will be finalised during checkout.
The labeled datasets used to support the findings of this study can be obtained from the corresponding author upon request.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Xiong 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
The authors acknowledge the support from REVA University for the facilities provided to carry out the research.
No funding received for this research.
Conflict of Interest
No conflict of interest.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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.
About this article
Cite this article
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