Treffer: INFORMATION TECHNOLOGY FOR AUTOMATION OF SERVER INFRASTRUCTURE MANAGEMENT USING DEVOPS TOOLS.

This research presents an automated server infrastructure management system integrating Python, Terraform, Ansible, MySQL, and the DigitalOcean API for dynamic DNS management, tailored for educational environments requiring rapid provisioning of uniform server configurations. It automates server deployment on the Hetzner platform, configuration standardization, and horizontal and vertical scaling. Objective to develop a scalable, automated infrastructure management system that can adapt to dynamic educational and operational requirements. Methodology: Python scripts have been utilized to generate Terraform configurations, thereby facilitating the creation of servers within the Hetzner cloud provider. The script employs the DigitalOcean API to automate Domain Name System (DNS) records, while Ansible is employed to ensure consistent server configurations. MySQL plays a pivotal role in providing real-time infrastructure monitoring and scaling. Scientific Novelty: The proposed system represents a significant advance in the field of scientific innovation by addressing the critical issue of infrastructure as code (IaC) optimization. It achieves this advancement by employing a formal M/G/с queue model, a methodical approach that has been empirically validated through analytical and experimental analyses. The efficacy of this model is evident in its ability to reduce deployment time by 50% compared to conventional IaC tools such as Puppet, Chef, and Ansible. Furthermore, its superior performance is pronounced, with a 90% reduction in deployment time when compared to manual methods. Results: The results of the experiment show that when using the Terraform infrastructure management tool, the deployment time of computing nodes remains unchanged regardless of their number. Specifically, deploying both two and five servers on the Hetzner platform takes an average of 270 seconds. This indicates a high degree of process parallelism and the scalability of the solution at this stage of infrastructure initialization. The configuration process is completed in 30-40 seconds. These results indicate a 90% reduction in configuration errors and an 80% reduction in costs for deploying 100 servers per month for laboratory or test tasks. The script allows for the execution of server templates only when necessary, for example, during laboratory sessions. The startup time is 4 minutes and 30 seconds, which enables the rapid provision of a working number of servers, sites, or applications for training. Conclusions: The system has been shown to enhance deployment efficiency, reduce operating costs, and broaden the range of possible applications in education, scientific research, and business. Future Research: Planned enhancements include multi-cloud integration (AWS, Google Cloud) for improved resilience, Kubernetes orchestration for containerized workloads, a web-based management interface to enhance usability, and machine learning-based predictive analytics for optimized resource scaling. These upgrades will expand the system's flexibility and applicability. [ABSTRACT FROM AUTHOR]

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