Main Article Content
Subject of research: methods of resource allocation of the cloud environment. The purpose of the research: to develop a method of resource allocation that will improve the security of the cloud environment. At the same time, effective data processing should be achieved. Method characteristics. The article discusses the method of adaptive resource allocation in cloud environments, focusing on its significance for data processing and enhanced security. A notable feature of the method is the consideration of external influences when calculating the characteristics of cloud resource requests and predicting resource requests based on a time series test. The main idea of this approach lies in the ability to intelligently distribute resources while considering real needs, which has the potential to optimize both productivity and confidentiality protection simultaneously. Integrating adaptive resource allocation methods not only improves data processing efficiency in cloud environments but also strengthens mechanisms against potential cyber threats. Research results. To ensure timely resource allocation, the NSGA-II algorithm has been enhanced. This allowed reducing the resolution time of multi-objective optimization tasks by 5%. Additionally, research results demonstrate that effective utilization of various types of resources on a physical machine reduces resource losses by 1.2 times compared to SPEA2 and NSGA-II methods.
Mezni, H., Aridhi, S.and Hadjali, A. (2018), “The uncertain cloud: State of the art and research challenges”, International Journal of Approximate Reasoning, Vol. 103, pp. 139-151, doi: https://doi.org/10.1016/j.ijar.2018.09.009.
Nawrocki, P., Grzywacz, M. and Sniezynski, B. (2021), “Adaptive resource planning for cloud-based services using machine learning”, Journal of Parallel and Distributed Computing, Vol. 152, pp. 88-97, doi: https://doi.org/10.1016/j.jpdc.2021.02.018.
Saidi, K., Hioual, O. and Siam, A. (2020), “Resources Allocation in Cloud Computing: A Survey”, ICAIRES 2019: Smart Energy Empowerment in Smart and Resilient Cities”, pp 356–364, doi: https://doi.org/10.1007/978-3-030-37207-1_37.
Habiba, U., Masood, R., Shibli, M.A. and Niazi, M.A. (2014), “Cloud identity management security issues & solutions: a taxonomy”, Complex Adapt Syst Model, Vol. 2, 5, doi: https://doi.org/10.1186/s40294-014-0005-9.
Kuchuk, N., Mozhaiev, O., Semenov, S., Haichenko, A., Kuchuk, H., Tiulieniev, S., Mozhaiev, M., Davydov, V., Brusakova, O. and Gnusov, Y. (2023), “Devising a method for balancing the load on a territorially distributed foggy environment”, Eastern-European Journal of Enterprise Technologies, 1(4 (121), pp. 48–55. doi: https://doi.org/10.15587/1729-4061.2023.274177.
Chen, J., Wang, Y. and Liu, T. (2021), “A proactive resource allocation method based on adaptive prediction of resource requests in cloud computing”, J. Wireless Com Network, 24, doi: https://doi.org/10.1186/s13638-021-01912-8.
Petrovska, I. and Kuchuk, H. (2022), “Static allocation method in a cloud environment with a service model IAAS”, Advanced Information Systems, vol. 6, is. 3, pp. 99–106, doi: https://doi.org/10.20998/2522-9052.2022.3.13.
Kuchuk, N., Shefer, O., Cherneva, G. and Alnaeri, F.A. (2021), “Determining the capacity of the self-healing network segment”, Advanced Information Systems, vol. 5, no. 2, pp. 114–119, Jun. 2021, doi: https://doi.org/10.20998/2522-9052.2021.2.16.
Petrovska, I. and Kuchuk H. (2022), “Features of the distribution of computing resources in cloud systems”, Control, Navigation and Communication Systems, No. 2, pp. 75-78, doi: http://dx.doi.org/10.26906/SUNZ.2022.2.075.
Kuchuk, G., Kovalenko, A., Komari, I.E., Svyrydov, A. and Kharchenko, V. (2019), “Improving big data centers energy efficiency: Traffic based model and method”, Studies in Systems, Decision and Control, vol. 171, Kharchenko, V., Kondratenko, Y., Kacprzyk, J. (Eds.), Springer Nature Switzerland AG, pp. 161-183, doi: https://doi.org/10.1007/978-3-030-00253-4_8.
Nechausov, A., Mamusuĉ, I. and Kuchuk, N. (2017), “Synthesis of the air pollution level control system on the basis of hyperconvergent infrastructures”, Advanced Information Systems, vol. 1, no. 2, , pp. 21–26, doi: https://doi.org/10.20998/2522-9052.2017.2.04.
Tan, B., Ma, H. and Mei, Y. (2017), “A NSGA-II-based approach for service resource allocation in cloud”, IEEE Congress on Evolutionary Computation (CEC), 17013723, pp. 2574–2581, doi: https://doi.org/10.1109/CEC.2017.7969618.
Mohamed, Abdel-Basset, Laila, Abdel-Fatah and Arun Kumar Sangaiah. (2018), “Chapter 10 - Metaheuristic Algorithms: A Comprehensive Review”, Editor(s): Arun Kumar Sangaiah, Michael Sheng, Zhiyong Zhang, Intelligent Data-Centric Systems, Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications, Academic Press, 2018, Pages 185-231, ISBN 9780128133149, doi: https://doi.org/10.1016/B978-0-12-813314-9.00010-4.
Liu, Xi, and Dan Zhang. 2019. "An Improved SPEA2 Algorithm with Local Search for Multi-Objective Investment Decision-Making" Applied Sciences 9, no. 8: 1675. doi: https://doi.org/10.3390/app9081675.