References
- Masdari, M., Gharehpasha, S., Ghobaei-Arani, M., & Ghasemi, V. (2020). Bioinspired virtual machine placement schemes in cloud computing environment: taxonomy, review, and future research directions. Cluster Computing, 23(4), 2533-2563. https://doi.org/10.1007/s10586-019-03026-9
- Gabhane, J. P., Pathak, S., & Thakare, N. M. (2021). Metaheuristics Algorithms for Virtual Machine Placement in Cloud Computing Environments-A Review. Computer Networks, Big Data and IoT, 329-349.
- Zheng Q et al (2016) Virtual machine consolidated placement based on multiobjective biogeography-based optimization. Future Gener Computer Syst 54:95-122 https://doi.org/10.1016/j.future.2015.02.010
- Da Cunha Rodrigues G et al (2016) Monitoring of cloud computing environments: concepts, solutions, trends, and future directions. In: Proceedings of the 31st annual ACM symposium on applied computing
- Fatima A et al (2019) An enhanced multi-objective gray wolf optimization for virtual machine placement in cloud data centers. Electronics 8(2):218 https://doi.org/10.3390/electronics8020218
- Ghobaei-Arani, Mostafa, Shamsi, Mahboubeh, Rahmanian, Ali A.: An efficient approach for improving virtual machine placement in cloud computing environment. J. Exp. Theor. Artif. Intell. 29(6), 1149-1171 (2017) https://doi.org/10.1080/0952813X.2017.1310308
- Abreu DP et al (2019) A comparative analysis of simulators for the cloud to fog continuum. Simul Modell Pract Theor:102029
- Chowdhury MR, Mahmud MR, Rahman RM(2015)Implementationandperformanceanalysis of various VM placement strategies in CloudSim. J Cloud Comput 4(1):20 https://doi.org/10.1186/s13677-015-0045-5
- Beloglazov A, Buyya R(2012)Optimalonlinedeterministicalgorithmsandadaptiveheuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurr Comput Practice Experience 24(13):1397-1420 https://doi.org/10.1002/cpe.1867
- Donyagard Vahed, N., Ghobaei-Arani, M., & Souri, A. (2019). Multiobjective virtual machine placement mechanisms using nature-inspired metaheuristic algorithms in cloud environments: A comprehensive review. International Journal of Communication Systems, 32(14), e4068. https://doi.org/10.1002/dac.4068
- Li Z, Li Y, Yuan T, Chen S, Jiang S. Chemical reaction optimization for virtual machine placement in cloud computing. Appl Intell. 2019;49(1):220-232. https://doi.org/10.1007/s10489-018-1264-5
- Beloglazov A, Buyya R, Lee YC, Zomaya A. A taxonomy and survey of energy-efficient data centers and cloud computing systems. In: Advances in Computers. Vol.82 Elsevier; 2011:47-111. https://doi.org/10.1016/B978-0-12-385512-1.00003-7
- Khan MA, Paplinski A, Khan AM, Murshed M, Buyya R. Dynamic virtual machine consolidation algorithms for energy-efficient cloud resource management: a review. In: Sustainable Cloud and Energy Services. Cham: Springer; 2018:135-165.
- Ahmad RW, Gani A, Hamid SHA, Shiraz M, Yousafzai A, Xia F. A survey on virtual machine migration and server consolidation frameworks for cloud data centers. J Netw Comput Appl. 2015;52:11-25. https://doi.org/10.1016/j.jnca.2015.02.002
- Varasteh A, Goudarzi M. Server consolidation techniques in virtualized data centers: a survey. IEEE Syst J. 2017;11(2):772-783. https://doi.org/10.1109/JSYST.2015.2458273
- Usmani Z, Singh S. A survey of virtual machine placement techniques in a cloud data center. Procedia Comput Sci. 2016;78:491-498. https://doi.org/10.1016/j.procs.2016.02.093
- Masdari M, Nabavi SS, Ahmadi V. An overview of virtual machine placement schemes in cloud computing. J Netw Comput Appl. 2016;66:106-127. https://doi.org/10.1016/j.jnca.2016.01.011
- Alboaneen, D.A., Tianfield, H. and Zhang, Y., 2016, July. Metaheuristic approaches to virtual machine placement in cloud computing: a review. In Parallel and Distributed Computing (ISPDC), 2016 15th International Symposium on (pp. 214-221). IEEE
- Xu M, Tian W, Buyya R. A survey on load balancing algorithms for virtual machines placement in cloud computing. Concurrency and Computation: Practice and Experience. 2017 Jun 25;29(12):e4123. https://doi.org/10.1002/cpe.4123
- Al-Dulaimy A, Itani W, Zekri A, Zantout R. Power management in virtualized data centers: state of the art. J Cloud Comput. 2016 Dec;5(1):6. https://doi.org/10.1186/s13677-016-0055-y
- Shahapure NH, Jayarekha P (2018) Distance and traffic based virtual machine migration for scalability in cloud computing. Proc Comput Sci 132:728-737 https://doi.org/10.1016/j.procs.2018.05.083
- Gahlawat, M., & Sharma, P. (2014, February). Survey of virtual machine placement in federated clouds. In 2014 IEEE International Advance Computing Conference (IACC) (pp. 735-738). IEEE.
- Buyya, R., A. Beloglazov, and J. Abawajy, Energy-efficient management of data center resources for cloud computing: a vision, architectural elements, and open challenges. arXiv preprint arXiv:1006.0308, 2010.
- Lopez-Pires, F., & Baran, B. (2015). Virtual machine placement literature review. arXiv preprint arXiv:1506.01509.
- Alashaikh, A., Alanazi, E., & Al-Fuqaha, A. (2021). A Survey on the Use of Preferences for Virtual Machine Placement in Cloud Data Centers. ACM Computing Surveys (CSUR), 54(5), 1-39. https://doi.org/10.1145/3450517
- Attaoui W, Sabir E (2018) Multi-criteria virtual machine placement in cloud computing environments: a literature review. arXiv:1802.05113
- Rong, H., Zhang, H., Xiao, S., Li, C., & Hu, C. (2016). Optimizing energy consumption for data centers. Renewable and Sustainable Energy Reviews, 58, 674-691. https://doi.org/10.1016/j.rser.2015.12.283
- Agarwal M, Srivastava GMS (2016) A genetic algorithm inspired task scheduling in cloud computing. Proc IEEE Int Conf Comput Com-mun Autom
- Masdari, M., Salehi, F., Jalali, M., & Bidaki, M. (2017). A survey of PSObased scheduling algorithms in cloud computing. Journal of Network and Systems Management, 25(1), 122-158. https://doi.org/10.1007/s10922-016-9385-9
- Ebadifard, F., & Babamir, S. M. (2017, April). Optimizing multi objective based workflow scheduling in cloud computing using black hole algorithm. In 2017 3th International Conference on Web Research (ICWR) (pp. 102-108). IEEE.
- Pietri, I., Chronis, Y., & Ioannidis, Y. (2017, December). Multi-objective optimization of scheduling dataflows on heterogeneous cloud resources. In 2017 IEEE International Conference on Big Data (Big Data) (pp. 361-368). IEEE.
- Beloglazov, A., Abawajy, J., & Buyya, R. (2012). Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future generation computer systems, 28(5), 755-768. https://doi.org/10.1016/j.future.2011.04.017
- Li X, Qian Z, Lu S, Wu J. Energy efficient virtual machine placement algorithm with balanced and improved resource utilization in a data center. Math Comput Model. 2013;58(5-6):1222-1235. https://doi.org/10.1016/j.mcm.2013.02.003
- Jamali S, Malektaji S, Analoui M. An imperialist competitive algorithm for virtual machine placement in cloud computing. J Exp Theor Artif Intell. 2017;29(3):575-596. https://doi.org/10.1080/0952813X.2016.1212101
- Xu J, Fortes J. A multi-objective approach to virtual machine management in datacenters. In: Proceedings of the 8th ACM International Conference on Autonomic Computing. ACM; 2011, June:225-234.
- Addya SK, Turuk AK, Sahoo B, Sarkar M, Biswash SK. Simulated annealing based VM placement strategy to maximize the profit for cloud service providers. Int J Eng Sci Technol. 2017;20(4):1249-1259.
- Geronimo, G.A., Uriarte, R.B. and Westphall, C.B., 2016, April. Order@ Cloud: A VM organization framework based on multi-objectives placement ranking. In Network Operations and Management Symposium (NOMS), 2016 IEEE/IFIP (pp. 529-535). IEEE.
- Baalamurugan KM, Bhanu SV. A multi-objective krill herd algorithm for virtual machine placement in cloud computing. J Supercomput. 2018;1-18.
- Mollamotalebi M, Hajireza S. Multi-objective dynamic management of virtual machines in cloud environments. J Cloud Comput. 2017;6(1):16. https://doi.org/10.1186/s13677-017-0086-z
- Song A, Fan W, Wang W, Luo J, Mo Y. Multi-objective virtual machine selection for migrating in virtualized data centers. In: Joint International Conference on Pervasive Computing and the Networked World. Berlin, Heidelberg: Springer; 2012, November:426-438.
- Xu, J. and Fortes, J.A., 2010, December. Multi-objective virtual machine placement in virtualized data center environments. In Green Computing and Communications (GreenCom), 2010 IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) (pp. 179-188). IEEE.
- Wang, S., Gu, H. and Wu, G., 2013, July. A new approach to multi-objective virtual machine placement in virtualized data center. In Networking, Architecture and Storage (NAS), 2013 IEEE Eighth International Conference on (pp. 331-335). IEEE.
- Wang X, Wang Y, Cui Y. A new multi-objective bi-level programming model for energy and locality aware multi-job scheduling in cloud computing. Futur Gener Comput Syst. 2014;36:91-101. https://doi.org/10.1016/j.future.2013.12.004
- Liu, C., Shen, C., Li, S. and Wang, S., 2014, June. A new evolutionary multiobjective algorithm to virtual machine placement in virtualized data center. In Software Engineering and Service Science (ICSESS), 2014 5th IEEE International Conference on (pp. 272-275). IEEE.
- Sofia AS, Kumar G. P. Multi-objective task scheduling to minimize energy consumption and makespan of cloud computing using NSGA-II. J Netw Syst Manag. 2018;26(2):463-485. https://doi.org/10.1007/s10922-017-9425-0
- Riahi M, Krichen S. A multi-objective decision support framework for virtual machine placement in cloud data centers: a real case study. J Supercomput. 2018;74(7):1-32. https://doi.org/10.1007/s11227-017-2102-y
- 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):1230-1242. https://doi.org/10.1016/j.jcss.2013.02.004
- Malekloo M, Kara N. Multi-objective ACO virtual machine placement in cloud computing environments. In: Globecom Workshops (GC Wkshps), 2014. IEEE; 2014, December:112-116.
- Malekloo MH, Kara N, El Barachi M. An energy efficient and SLA compliant approach for resource allocation and consolidation in cloud computing environments. Sustainable Computing: Informatics and Systems. 2018;17:9-24. https://doi.org/10.1016/j.suscom.2018.02.001
- Tan M, Chi C, Zhang J, Zhao S, Li G, Lu S. An energy-aware virtual machine placement algorithm in cloud data center. In: Proceedings of the 2nd International Conference on Intelligent Information Processing. ACM; 2017, July:1.
- Zhang L, Wang Y, Zhu L, Ji W. Towards energy efficient cloud: an optimized ant colony model for virtual machine placement. J Commun Inf Networks. 2016;1(4):116-132. https://doi.org/10.1007/bf03391585
- Pham NMN, Le VS. Applying ant colony system algorithm in multi-objective resource allocation for virtual services. J Inf Telecommun. 2017;1(4):319-333. https://doi.org/10.1080/24751839.2017.1356159
- Ashraf A, Porres I. Multi-objective dynamic virtual machine consolidation in the cloud using ant colony system. Int J Parallel Emergent Distrib Syst. 2018;33(1):103-120. https://doi.org/10.1080/17445760.2017.1278601
- Zheng, Q., Li, R., Li, X. and Wu, J., 2015, May. A multi-objective biogeography-based optimization for virtual machine placement. In Cluster, Cloud and Grid Computing (CCGrid), 2015 15th IEEE/ACM International Symposium on (pp. 687-696). IEEE.
- Zheng Q, Li R, Li X, et al. Virtual machine consolidated placement based on multi-objective biogeography-based optimization. Futur Gener Comput Syst. 2016;54:95-122. https://doi.org/10.1016/j.future.2015.02.010
- Li R, Zheng Q, Li X, Yan Z. Multi-objective optimization for rebalancing virtual machine placement. Futur Gener Comput Syst. 2017.
- Ramezani, F., Naderpour, M. and Lu, J., 2016, July. 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). IEEE.
- Luo J, Song W, Yin L. Reliable virtual machine placement based on multiobjective optimization with traffic-aware algorithm in industrial cloud. IEEE Access. 2018;6:23043-23052. https://doi.org/10.1109/access.2018.2816983
- Xu B, Peng Z, Xiao F, Gates AM, Yu JP. Dynamic deployment of virtual machines in cloud computing using multi-objective optimization. Soft Comput. 2015;19(8):2265-2273. https://doi.org/10.1007/s00500-014-1406-6
- Guo L, He Z, Zhao S, Zhang N, Wang J, Jiang C. Multi-objective optimization for data placement strategy in cloud computing. In: International Conference on Information Computing and Applications. Berlin, Heidelberg: Springer; 2012, September:119-126.
- Li Z, Yan C, Yu L, Yu X. Energy-aware and multi-resource overload probability constraint-based virtual machine dynamic consolidation method. Futur Gener Comput Syst. 2018;80:139-156. https://doi.org/10.1016/j.future.2017.09.075
- Lopez-Pires F, Baran B. 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; 2013, December:203-210.
- Lopez-Pires F, Baran B. Many-objective optimization for virtual machine placement in cloud computing. In: Research Advances in Cloud Computing. Singapore: Springer; 2017:291-326.
- Li, X.K., Gu, C.H., Yang, Z.P. and Chang, Y.H., 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 (pp. 61-66). IEEE.
- Ding W, Gu C, Luo F, et al. DFA-VMP: an efficient and secure virtual machine placement strategy under cloud environment. Peer-to-Peer Network Appl. 2018;11(2):318-333. https://doi.org/10.1007/s12083-016-0502-z
- Chang, Y., Gu, C. and Luo, F., 2016, October. A novel energy-aware and resource efficient virtual resource allocation strategy in IaaS cloud. In Computer and Communications (ICCC), 2016 2nd IEEE International Conference on (pp. 1283-1288). IEEE.
- Zheng, Q., Li, J., Dong, B., Li, R., Shah, N. and Tian, F., 2015, December. Multi-objective optimization algorithm based on BBO for virtual machine consolidation problem. In Parallel and Distributed Systems (ICPADS), 2015 IEEE 21st International Conference on (pp. 414-421). IEEE.
- Qian, B., Meng, F. and Chu, D., 2015, December. A cost-driven multiobjective optimization algorithm for SaaS applications placement. In Smart City/SocialCom/SustainCom (SmartCity), 2015 IEEE International Conference on (pp. 1086-1091). IEEE.
- Arianyan E, Taheri H, Khoshdel V. Novel fuzzy multi-objective DVFS-aware consolidation heuristics for energy and SLA efficient resource management in cloud data centers. J Netw Comput Appl. 2017;78:43-61. https://doi.org/10.1016/j.jnca.2016.09.016
- Saber T, Thorburn J, Murphy L, Ventresque A. VM reassignment in hybrid clouds for large decentralized companies: a multi-objective challenge. Futur Gener Comput Syst. 2018;79:751-764. https://doi.org/10.1016/j.future.2017.06.015
- Regaieg, R., Koubaa, M., Osei-Opoku, E. and Aguili, T., 2018, July. Multiobjective mixed integer linear programming model for VM placement to minimize resource wastage in a heterogeneous cloud provider data center. In 2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN) (pp. 401-406). IEEE.