Received 06.01.2025, Revised 14.03.2025, Accepted 24.04.2025

Adaptive performance monitoring in cloud environments via recurrent neural networks

Pavlo Kudrynskyi, Oleksandr Zvenihorodskyi

The study aimed to develop an adaptive methodology for analysing the performance of cloud computing infrastructures to improve the efficiency of resource management and reduce maintenance costs. The research addressed the implementation of the latest approaches to automate monitoring and analysis processes. The research methodology included the integration of data from monitoring platforms (Amazon Web Services CloudWatch, Google Cloud Monitoring, Prometheus) to collect key performance indicators. Data processing was conducted using Python libraries (NumPy, pandas, scikit-learn) to detect anomalies and generate time series. Recurrent neural networks and long-short-term memories based on TensorFlow and PyTorch were used to model performance. The implementation of continuous learning was used to adapt the models to the changing conditions of cloud systems in real-time. The main results of the study include the creation of an innovative system for predicting key performance metrics of cloud infrastructures with high accuracy. This was confirmed using the mean absolute error and root mean square error metrics. Real-time data integration was provided through the Amazon Kinesis platform, and visualisation and management were performed using Amazon CloudWatch and Grafana dashboards. Virtual machines and containers interacted with Nova, Glance, Cinder, and Neutron modules, and the Keystone module provided security through authentication and authorisation. Automatic resource scaling based on neural networks optimised the use of computing, network and storage resources. The developed methodology can be used to automate the management of cloud resources, reducing the need for manual intervention and cutting costs. The proposed method provided high speed due to interaction via REST and HTTPS and collected data in a time series format for primary processing. The integration of OpenStack with Apache Spark and the use of a high-speed data channel has increased the efficiency of the infrastructure. The findings demonstrated that the implementation of this methodology significantly increases the efficiency of cloud infrastructure management

cloud computing systems; resource performance; time series; continuous learning; cloud infrastructure optimisation; load forecasting
79-92
Kudrynskyi, P., & Zvenihorodskyi, O. (2025). Adaptive performance monitoring in cloud environments via recurrent neural networks. Information Technologies and Computer Engineering, 22(1), 79-92. https://doi.org/10.63341/vitce/1.2025.79

References

[1] Akindote, O.J., Adegbite, A.O., Dawodu, S.O., Omotosho, A., & Anyanwu, A. (2023). Innovation in data storage technologies: From cloud computing to edge computing. Computer Science & IT Research Journal, 4(3), 273-299. doi: 10.51594/csitrj.v4i3.661.

[2] Al-Jumaili, A.H., Muniyandi, R.C., Hasan, M.K., Paw, J.K., & Singh, M.J. (2023). Big data analytics using cloud computing based frameworks for power management systems: Status, constraints, and future recommendations. Sensors, 23(6), article number 2952. doi: 10.3390/s23062952.

[3] Anbalagan, K. (2024). AI in cloud computing: Enhancing services and performance. International Journal of Computer Engineering and Technology, 15(4), 622-635. doi: 10.5281/zenodo.13353681.

[4] Apeh, A.J., Hassan, A.O., Oyewole, O.O., Fakeyede, O.G., Okeleke, P.A., & Adaramodu, O.R. (2023). GRC strategies in modern cloud infrastructures: A review of compliance challenges. Computer Science & IT Research Journal, 4(2), 111-125. doi: 10.51594/csitrj.v4i2.609.

[5] Aslanpour, M.S., Gill, S.S., & Toosi, A.N. (2020). Performance evaluation metrics for cloud, fog and edge computing: A review, taxonomy, benchmarks and standards for future research. Internet of Things, 12, article number 100273. doi: 10.1016/j.iot.2020.100273.

[6] Bagai, R. (2024). Comparative analysis of AWS model deployment services. International Journal of Computer Trends and Technology, 72(5), 102-110. doi: 10.14445/22312803/IJCTT-V72I5P113.

[7] Baytelman, Y., & Potsepaiev, V. (2024). Development of a content management system and its cloud deployment. Scientific Papers of Donetsk National Technical University. Series: “Computer Engineering and Automation”, 2(34), 14-31. doi: 10.31474/2786-9024/v2i2(34).313761.

[8] Behlitsov, S. (2024). Software migration to a cloud architecture automation using an infrastructure as code tool Terraform AWS environment. Computer-Integrated Technologies: Education, Science, Production, 56, 99-106. doi: 10.36910/6775-2524-0560-2024-56-12.

[9] Chinamanagonda, S. (2023). Focus on resilience engineering in cloud services. Academia Nexus Journal, 2(1).

[10] Cluci, M.I., Pinzaru, C., Fotache, M., Rusu, O., & Gasner, P. (2023). OpenStack in higher education and academic research: A case study on benchmarking big data processing tools. In Proceedings of the international conference on advanced scientific computing (pp. 1-6). Cluj-Napoca: IEEE. doi: 10.1109/ICASC58845.2023.10328025.

[11] Dorosh, M., Hrek, I., & Buhai, Yu. (2020). Development of a model of automated personnel selection system using artificial intelligence methods. Technical Sciences and Technologies, 20(2), 158-166.

[12] Duan, T., Chen, R., Wang, P., Zhao, J., Liu, J., Han, S., Liu, Y., & Xu, F. (2025). BSODiag: A global diagnosis framework for batch servers outage in large-scale cloud infrastructure systems. ArXiv. doi: 10.48550/arXiv.2502.15728.

[13] Gumaste, S., Narayan, D.G., Shinde, S., & Amit, K. (2020). Detection of DDoS attacks in OpenStack-based private cloud using Apache Spark. Journal of Telecommunications and Information Technology, 82(4), 62-71. doi: 10.26636/ jtit.2020.146120.

[14] Ileana, M., Oproiu, M.I., & Marian, C.V. (2024). Using docker swarm to improve performance in distributed web systems. In Proceedings of the international conference on development and application systems (pp. 1-6). Suceava: IEEE. doi: 10.1109/DAS61944.2024.10541234.

[15] Islam, M.T., Srirama, S.N., Karunasekera, S., & Buyya, R. (2020). Cost-efficient dynamic scheduling of big data applications in Apache Spark on cloud. Journal of Systems and Software, 162, article number 110515. doi: 10.1016/j. jss.2019.110515.

[16] Krishnan, P., Jain, K., Aldweesh, A., Prabu, P., & Buyya, R. (2023). OpenStackDP: A scalable network security framework for SDN-based OpenStack cloud infrastructure. Journal of Cloud Computing, 12(1), article number 26. doi: 10.1186/ s13677-023-00406-w.

[17] Krishnaveni, S., Sivamohan, S., Sridhar, S.S., & Prabakaran, S. (2021). Efficient feature selection and classification through ensemble method for network intrusion detection on cloud computing. Cluster Computing, 24(3), 1761-1779. doi: 10.1007/s10586-020-03222-y.

[18] Kuprienko, A., & Galchynskyi, L. (2023). Agent-based model of access rights mining in cloud environments. In Proceedings of the 3rd international scientific and practical conference “Science and education in progress” (pp. 482490). Dublin: InterConf.

[19] Li, H., Wang, S.X., Shang, F., Niu, K., & Song, R. (2024). Applications of large language models in cloud computing: An empirical study using real-world data. International Journal of Innovative Research in Computer Science & Technology, 12(4), 59-69. doi: 10.55524/ijircst.2024.12.4.10.

[20] Li, L., Ke, X., Wang, G., & Shi, J. (2024). AI-enhanced security for large-scale Kubernetes clusters: Advanced defense and authentication for national cloud infrastructure. Journal of Theory and Practice of Engineering Science, 4(12), 25-38. doi: 10.5281/zenodo.14195743.

[21] Malallah, H.S., Qashi, R., Abdulrahman, L.M., Omer, M.A., & Yazdeen, A.A. (2023). Performance analysis of enterprise cloud computing: A review. Journal of Applied Science and Technology Trends, 4(1), 1-12. doi: 10.38094/jastt401139.

[22] Namasudra, S., Chakraborty, R., Kadry, S., Manogaran, G., & Rawal, B.S. (2021). FAST: Fast accessing scheme for data transmission in cloud computing. Peer-to-Peer Networking and Applications, 14, 2430-2442. doi: 10.1007/s12083-02000959-6.

[23] Nikitina, L., Dzheniuk, N., & Borysova, L. (2024). An expert system for cloud service risk assessment. Control, Navigation and Communication Systems. Academic Journal, 1(75), 146-151. doi: 10.26906/SUNZ.2024.1.146.

[24] Opirskyy, I., Vasylyshyn, S., & Susukailo, V. (2021). Investigating cybercrime with honeypots in the cloud. Ukrainian Scientific Journal of Information Security, 27(1), 20-26. doi: 10.18372/2225-5036.26.15574.

[25] Rahman, A., Ashrafuzzaman, M., Jim, M., & Sultana, R. (2024). Cloud security posture management automating risk identification and response in cloud infrastructures. Academic Journal on Science, Technology, Engineering & Mathematics Education, 4(3), 151-162. doi: 10.69593/ajsteme.v4i03.103.

[26] Shaffi, S.M. (2025). Transforming healthcare with real-time big data analytics: Opportunities, challenges, and future directions. International Journal for Multidisciplinary Research, 7(1). doi: 10.36948/ijfmr.2025.v07i01.36459.

[27] Tang, S., He, B., Yu, C., Li, Y., & Li, K. (2020). A survey on spark ecosystem: Big data processing infrastructure, machine learning, and applications. IEEE Transactions on Knowledge and Data Engineering, 34(1), 71-91. doi: 10.1109/ TKDE.2020.2975652.

[28] Varanitskyi, D., Rozkolodko, O., Liuta, M., Zakharova, M., & Hotunov, V. (2024). Analysis of data protection mechanisms in cloud environments. Technologies and Engineering, 25(1), 9-16. doi: 10.30857/2786-5371.2024.1.1.

[29] Vavilenkova, A. (2024). The threats from using cloud services in the field of cyber security. Electronic Professional Scientific Journal “Cybersecurity: Education, Science, Technique”, 2(26), 409-416. doi: 10.28925/2663-4023.2024.26.704.

[30] Zahvoyskyi, R.Y., & Kazymyra, I.Y. (2024). Monitoring complex computing systems using artificial intelligence tools. In International science-practical conference “Forestry education and science: Current challenges and development prospects”. doi: 10.36930/conf150.5.12.