Academic publishing in Europe and N. America

Archive Publication ethics Submission Payment Contacts
In the original languageTranslation into English

Economic efficiency of IT infrastructure engineering optimization in commercial companies

Authors

Ivan Otkidach

Rubric:Computer science
3
0
Quote
3
0

Annotation

This article examines the economic efficiency of engineering optimization of IT infrastructure in commercial companies. It analyzes the impact of technical solutions on operating costs, service availability, computing resource utilization, and losses associated with infrastructure failures and downtime. Modern optimization methods are considered, including virtualization, rightsizing, autoscaling, Infrastructure as Code, observability, and Site Reliability Engineering. Particular attention is given to current approaches such as FinOps, AIOps, platform engineering, and intelligent energy management. The role of engineering solutions in improving business process resilience and achieving an economically justified balance between infrastructure costs, performance, and service reliability is also examined.

Keywords

IT infrastructure
economic efficiency
engineering optimization
FinOps
AIOps
observability
autoscaling

Authors

Ivan Otkidach

References:

Bermejo, B., & Juiz, C. (2022) A general method for evaluating the overhead when consolidating servers: Performance degradation in virtual machines and containers. Journal of Supercomputing, 78, 11345–11372. https://doi.org/10.1007/s11227-022-04318-5

Chen, X., Guo, M., & Shangguan, W. (2022) Estimating the impact of cloud computing on firm performance: An empirical investigation of listed firms. Information & Management, 59 (3), 103603. https://doi.org/10.1016/j.im.2022.103603

Cheng, M., Qu, Y., Jiang, C., & Zhao, C. (2022) Is cloud computing the digital solution to the future of banking? Journal of Financial Stability, 63, 101073. https://doi.org/10.1016/j.jfs.2022.101073

Díaz-de-Arcaya, J., Torre-Bastida, A. I., Zárate, G., Miñón, R., & Almeida, A. (2024) A joint study of the challenges, opportunities, and roadmap of MLOps and AIOps: A systematic survey. ACM Computing Surveys, 56 (4), 84, 1–30. https://doi.org/10.1145/3625289

Entrialgo, J., García, M., García, J., López, J. M., & Díaz, J. L. (2024) Joint autoscaling of containers and virtual machines for cost optimization in container clusters. Journal of Grid Computing, 22 (1), 17. https://doi.org/10.1007/s10723-023-09732-4

Everman, B., Rajendran, N., Li, X., & Zong, Z. (2021) Improving the cost efficiency of large-scale cloud systems running hybrid workloads: A case study of Alibaba cluster traces. Sustainable Computing: Informatics and Systems, 30, 100528. https://doi.org/10.1016/j.suscom.2021.100528

Feitosa, D., Penca, M.-T., Berardi, M., Boza, R.-D., & Andrikopoulos, V. (2024) Mining for cost awareness in the infrastructure as code artifacts of cloud-based applications: An exploratory study. Journal of Systems and Software, 215, 112112. https://doi.org/10.1016/j.jss.2024.112112

Khlystun, M. (2026) LLM inference in high-load infrastructure: Architectural approaches and practical constraints. Cold Science, 29, 57–68.

Perelekhov, I. (2026) Designing fault-tolerant e-commerce systems: Architectural patterns and practical solutions. The Scientific Heritage, 189, 58–63. https://doi.org/10.5281/zenodo.21617973

Pintye, I., Kovács, J., & Lovas, R. (2024) Enhancing machine learning-based autoscaling for cloud resource orchestration. Journal of Grid Computing, 22 (4), 68. https://doi.org/10.1007/s10723-024-09783-1

State of FinOps 2025 Report. (2025) FinOps. [cited: 10.08.2026]. Available from https://data.finops.org/2025-report/

Uptime Institute. (2024) Uptime Institute Global Data Center Survey 2024. Uptime Institute Intelligence. 31 p.

Other articles of the issue

Farrukh Kholmurzaev, Mavjuda Eshkobilova DEVELOPMENT OF A SEMICONDUCTOR-BASED SELECTIVE SENSOR FOR ACETONE VAPOR MONITORING AND DETERMINATION OF ITS METROLOGICAL CHARACTERISTICS
134 views
cc-license
About us Journals Books
Publication ethics Terms of use of services Privacy policy
Copyright 2013-2025 Premier Publishing s.r.o.
Praha 8 - Karlín, Lyčkovo nám. 508/7, PSČ 18600, Czech Republic pub@ppublishing.org