Tato práce se zaměřuje na vytvoření webové komponenty k vykreslení grafů ze strukturovanýho datovýho formátu (dále jen schéma) a vytvoření uživatelského rozhraní pro editaci schématu pro Ansible Automation Analytics. Práce zkoumá aktuální implementaci Ansible Automation Analytics a odpovídající API. Dále zkoumá vhodné knihovny pro vykreslování grafů a popisuje základy použitých technologií. Praktická část popisuje požadavky na komponentu a popisuje vývoj a implementaci pluginu. Dále práce popisuje proces testování a~plány budoucího vývoje pluginu.

This master's thesis deals with a system for PC mass management and monitoring. There is a brief description of Puppet, Chef and Ansible, which are tools enabling this functionality. The overall system issues are solved by Ansible. This thesis also includes a script created for a classroom’s remote management according to a beforehand fixed assignment. The system enables remote installation and configuration of applications, creation of users, editing the files, update of the system and many more. A web application was created for this system. Through this application, the system can be controlled via web browser, both from local and public network. Practical examples of the issue dealt with is also part of the thesis.

This work is focused on the security of small to medium-sized networks with central administration, especially on the creation of a methodology for secure network design.The design of a secure network for a start-up IT company, using open-source software, is described. Deployment of the designed secure network, with central management, is performed and the connectivity of network elements are tested. The model simulates network traffic situations and network attacks using penetration testing techniques. In terms of the severity of the impact on network security, intercepted attacks are evaluated and immediately reported to responsible persons. Finally, the results of the intercepted attacks are processed and further actions are recommended.

This master thesis focuses on the issue of automation of virtual infrastructure and available tools such as Ansible, Terraform and Puppet. The individual tools are compared here in sequence for installations or configuration creation. The result of this semester thesis is the selection of a suitable tool for automation of virtual infrastructure.

As IT platforms grow larger and more complex, so does the underlying infrastructure. Virtualization is an essential factor for more efficient resource allocation, improving both the management and environmental impact. It allows more robust solutions and facilitates the use of IaC (infrastructure ascode). Many systems developed today consist of containerized microservices. Considered the standard of container orchestration, Kubernetes is the natural next step for many companies. But how do we move on from previous solutions to a Kubernetes cluster? We found that there are not a lot of detailed enough guidelines available, and set out to gain more knowledge by diving into the subject - implementing prototypes that would act as a foundation for a resulting guideline of how it can be done.

<p dir="ltr">Recent advancements in large language models such as ChatGPT and AU Large allow for the effective integration and application of LLMs into network devices such as switches and routers in terms of the ability to play a role in configuration and management. The given devices are an essential part of every network infrastructure, and the nature of physical networking topologies is complex, which leads to the need to ensure optimal network efficiency and security via meticulous and precise configurations.</p><p dir="ltr">The research explores the potential of an AI-driven interface that utilizes AU Large to streamline, enhance, and automate the configuration process of network devices while ensuring that the security of the whole process is guaranteed by running the entire system on-premise. Three core areas are of primary concern in the given study: the effectiveness of integrating the AU Large into network management systems, the impact on efficiency, accuracy, and error rates in network configurations, and the scalability and adaptability to more complex requirements and growing network environments.</p><p dir="ltr">The key performance metrics evaluated are the error rate in the generated configurations, scalability by looking at the performance as more network devices are added, and the ability to generate incredibly complex configurations accurately. The high-level results of the critical performance metrics show an evident correlation between increased device count and increased prompt complexity with a degradation in the performance of the AU Large model from Mistral AI.</p><p dir="ltr">This research has significant potential to alter preset network management practices by applying AI to make network configuration more efficient, reduce the scope for human error, and create an adaptable tool for diverse and complex networking environments. This research contributes to both AI and network management fields by highlighting a path toward the “future of network management.”</p>