[
1. Shynu, P. G., K. J. Singh. A Comprehensive Survey and Analysis on Access Control Schemes in Cloud Environment. – Cybernetics and Information Technologies, Vol. 16, 2016, No 1, pp. 19-38.10.1515/cait-2016-0002
]Search in Google Scholar
[
2. Beloglazov, A., R. Buyya. Managing Overloaded Hosts for Dynamic Consolidation of Virtual Machines in Cloud Data Centers under Quality-of-Service Constraints. – IEEE Transactions on Parallel and Distributed Systems, 2013. DOI: 10.1109/TPDS.2012.240.10.1109/TPDS.2012.240
]Search in Google Scholar
[
3. Puhan, S., D. Panda, B. K. Mishra. Energy Efficiency for Cloud Computing Applications: A Survey on the Recent Trends and Future Scopes. IEEE Xplore, 2020.10.1109/ICCSEA49143.2020.9132878
]Search in Google Scholar
[
4. Wang, H., H. Tianfield. Energy-Aware Dynamic Virtual Machine Consolidation for Cloud Datacenters. – IEEE, Vol. 6, 2018, pp. 15259-15273. DOI: 10.1109/ACCESS.2018.2813541.10.1109/ACCESS.2018.2813541
]Search in Google Scholar
[
5. Beloglazov, A., R. Buyya. Optimal Online Deterministic Algorithms and Adaptive Heuristics for Energy and Performance Efficient Dynamic Consolidation of Virtual Machines in Cloud Datacenters. – Concurr. Comput. Pract., 2013, pp. 1397-1420. https://doi.org/10.1002/cpe.186710.1002/cpe.1867
]Search in Google Scholar
[
6. Masdar, M., M. Zangakani. Green Cloud Computing Using Proactive Virtual Machine Placement: Challenges and Issues, Springer Nature B. V. 2019. – J. Grid Computing. https://doi.org/10.1007/s10723-019-09489-910.1007/s10723-019-09489-9
]Search in Google Scholar
[
7. Lee, H. M., Y. Jeong, H. J. Jang. Performance Analysis-Based Resource Allocation for Green Cloud. – J. Supercomput., Vol. 69, 2014, pp. 1013-1026. https://doi.org/10.1007/s11227-013-1020-x10.1007/s11227-013-1020-x
]Search in Google Scholar
[
8. Esfandiarpoor, S., A. Pahlavan, M. Goudarzi. Structure-Aware Online Virtual Machine Consolidation for Datacenter Energy Improvement in Cloud Computing. – Comput. Electr. Eng., Vol. 42, 2015. https://doi.org/10.1016/j.compeleceng.2014.09.00510.1016/j.compeleceng.2014.09.005
]Search in Google Scholar
[
9. Madhumala, R. B., H. Tiwari, C. Devaraj Verma. Virtual Machine Placement Using Energy – Efficient Particle Swarm Optimization in Cloud Datacenter. – Cybernetics and Information Technologies, Vol. 21, 2021, No 1, pp. 62-72.10.2478/cait-2021-0005
]Search in Google Scholar
[
10. Ferreto, T. C., M. A. S. Netto, R. N. Calherious, C. A. F. De Rsoe. Server Consolidation with Migration Control for Virtualized Data Centers. – Future Generation Computer Systems, October 2011. https://doi.org/10.1016/j.future.2011.04.01610.1016/j.future.2011.04.016
]Search in Google Scholar
[
11. Bruno, B. C., C. Ribas, R. M. Suguimoto, R. A. N. R. Montaño, F. Silva, L. D. Bona, M. A. Castilho. On Modelling Virtual Machine Consolidation to Pseudo-Boolean Constraints. – J. Pavon, Ed. 2012. pp. 361-370. https://doi.org/10.1007/978-3-642
]Search in Google Scholar
[
12. Fard, S. Y. Z., M. R. Ahmadi, S. Adabi. A Dynamic VM Consolidation Technique for QoS and Energy Consumption in Cloud Environment. – J. Supercomput., Vol. 73, 2017, pp. 4347-4368. https://doi.org/10.1007/s11227-017-2016-810.1007/s11227-017-2016-8
]Search in Google Scholar
[
13. Kumar, M. R. V., S. Raghunathan. Heterogeneity and Thermal Aware Adaptive Heuristics for Energy Efficient Consolidation of Virtual Machines in Infrastructure Clouds. – Journal of Computer and System Sciences, March 2016. https://doi.org/10.1016/j.jcss.2015.07.00510.1016/j.jcss.2015.07.005
]Search in Google Scholar
[
14. Arianyan, E., H. Taheri, S. Sharifian. Novel Energy and SLA Efficient Resource Management Heuristics for Consolidation of Virtual Machines in Cloud Data Centers. – Comput. Electr. Eng., Vol. 47, 2015, pp. 222-240. https://doi.org/10.1016/j.compeleceng.2015.05.00610.1016/j.compeleceng.2015.05.006
]Search in Google Scholar
[
15. Okada, T. K., A. De La Fuente Vigliotti, D. M. Batista, A. Goldman vel Lejbman. Consolidation of VMs to Improve Energy Efficiency in Cloud Computing Environments. – In: Proc. of 2015 XXXIII Brazilian Symposium on Computer Networks and Distributed Systems, Vitoria, 2015, pp. 150-158. DOI: 10.1109/SBRC.2015.27.10.1109/SBRC.2015.27
]Search in Google Scholar
[
16. Kollu, A., V. Sucharita. Energy-Aware Multi-Objective Differential Evolution in Cloud Computing. – In: S. Dash, S. Das, B. Panigrahi, Eds. Proc. of International Conference on Intelligent Computing and Applications. Advances in Intelligent Systems and Computing. Vol. 632. Singapore, Springer, 2017. https://doi.org/10.1007/978-981-10-5520-1_4010.1007/978-981-10-5520-1_40
]Search in Google Scholar
[
17. Horri, A., M. S. Mozafari, G. Dastghaibyfard. Novel Resource Allocation Algorithms to Performance and Energy Efficiency in Cloud Computing. – J. Supercomput., Vol. 69, 2014, pp. 1445-1461. https://doi.org/10.1007/s11227-014-1224-810.1007/s11227-014-1224-8
]Search in Google Scholar
[
18. Mandal, R., M. K. Mondal, S. Banerjee, U. Biswas. An Approach toward Design and Development of an Energy‐Aware VM Selection Policy with Improved SLA Violation in the Domain of Green Cloud Computing. Springer Science+Business Media, LLC, Part of Springer Nature 2020, The Journal of Supercomputing. https://doi.org/10.1007/s11227-020-03165-610.1007/s11227-020-03165-6
]Search in Google Scholar
[
19. Wood, T., P. Shenoy, A. Venkataramani, M. Yousif. Black-Box and Gray-Box Strategies for Virtual Machine Migration. – In: Proc. of 4th USENIX Symposium on Networked Systems Design Implementation (NSDI’07), 11-13 April 2007, USA, pp. 229-242.
]Search in Google Scholar
[
20. Tian, W., Y. Zhao, Y. Zhong, M. Xu, C. Jing. A Dynamic and Integrated Load-Balancing Scheduling Algorithm for Cloud Datacenters. – In: Proc. of 2011 IEEE International Conference on Cloud Computing and Intelligence Systems, 2011, pp. 311-315. DOI: 10.1109/CCIS.2011.6045081.10.1109/CCIS.2011.6045081
]Search in Google Scholar
[
21. Lin, X., Z. Liu, W. Guo. Energy-Efficient VM Placement Algorithms for Cloud Data Center. – In: W. Qiang, X. Zheng, C. H. Hsu, Eds. Proc. of Cloud Computing and Big Data. CloudCom-Asia 2015. Vol. 9106. Cham, Springer, 2015. https://doi.org/10.1007/978-3-319-28430-9_410.1007/978-3-319-28430-9_4
]Search in Google Scholar
[
22. Greenberg, A., D. Hamilton, A. Maltz, P. Patel. The Cost of a Cloud Research Problems in Data Centers Networks. – In: Proc. of ACM SICOMM, Vol. 39, 2009, No 1, pp. 68-73.10.1145/1496091.1496103
]Search in Google Scholar
[
23. Khosravi, A., S. K Garg., R. Buyya. Energy and Carbon-Efficient Placement of Virtual Machines in Distributed Cloud Data Centers. – In: F. Wolf, B. Mohr, D. an Mey, Eds. Proc. of Euro-Par 2013 Parallel Processing. Euro-Par 2013. Lecture Notes in Computer Science, Vol. 8097. Berlin, Heidelberg, Springer, 2013. https://doi.org/10.1007/978-3-642-40047-6_3310.1007/978-3-642-40047-6_33
]Search in Google Scholar
[
24. Mazzucco, M., D. Dyachuk, R. Deters. Maximizing Cloud Providers’ Revenues via Energy Aware Allocation Policies. – In: Proc. of 2010 IEEE 3rd International Conference on Cloud Computing, Miami, FL, 2010, pp. 131-138. DOI: 10.1109/CLOUD.2010.68.10.1109/CLOUD.2010.68
]Search in Google Scholar
[
25. Rivoire, S., P. Ranganathan, C. Kozyrakis. A Comparison of High-Level Full-System Power Models. – In: Proc. of 2008 Conference on Power Aware Computing and Systems (HotPower’08), USENIX Association, USA.
]Search in Google Scholar
[
26. Kavanagh, R., D. Armstrong, K. Djemame, D. Sommacampagna, L. Blasi. Towards an Energy-Aware Cloud Architecture for Smart Grids. – In: J. Altmann, G. Silaghi, O. Rana, Eds. Proc. of Economics of Grids, Clouds, Systems, and Services (GECON’15). Vol. 9512. Cham, Springer, 2015. https://doi.org/10.1007/978-3-319-43177-2_1310.1007/978-3-319-43177-2_13
]Search in Google Scholar
[
27. Voorsluys, W., J. Broberg, S. Venugopal, R. Buyya. Cost of Virtual Machine Live Migration in Clouds: A Performance Evaluation. – In: M. G. Jaatun, G. Zhao, C. Rong, Eds. Proc. of Cloud Computing. CloudCom 2009. Lecture Notes in Computer Science. Vol. 5931. Berlin, Heidelberg, Springer. https://doi.org/10.1007/978-3-642-10665-1_2310.1007/978-3-642-10665-1_23
]Search in Google Scholar
[
28. Hongyou, L., W. Jiangyong, P. Jian, W. Junfeng, L. Tang. Energy-Aware Scheduling Scheme Using Workload-Aware Consolidation Technique in Cloud Data Centres. – China Communications, Vol. 10, 2013, No 12, pp. 114-124.10.1109/CC.2013.6723884
]Search in Google Scholar
[
29. Simarro, J. L. L., R. M. Vozmediano, R. S. Montero, I. M. Liorente. Scheduling Strategies for Optimal Service Deployment across Multiple Clouds. – Future Generation Computer Systems, Vol. 29, 2013, No 6, pp. 1431-1441. https://doi.org/10.1016/j.future.2012.01.00710.1016/j.future.2012.01.007
]Search in Google Scholar
[
30. Calheiros, R. N., R. Ranjan, A. Beloglazov, De C. A. F. Rose, R. Buyya. CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms. – Softw. Pract. Exp., Vol. 41, 2010, No 1, pp. 23-50, https://doi.org/10.1002/spe.99510.1002/spe.995
]Search in Google Scholar
[
31. https://www.spec.org/power_ssj/results/
]Search in Google Scholar
[
32. https://aws.amazon.com/ec2/instance-types/
]Search in Google Scholar
[
33. Park, S., V. Pai. CoMon Monitoring System for Planet Lab. – ACM SIGOPS Operating Systems Review, Vol. 40, January 2006, Issue 1, pp. 65-74. https://doi.org/10.1145/1113361.111337410.1145/1113361.1113374
]Search in Google Scholar
