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Automating the deployment, security risk minimization, scaling, maintenance and development processes is highly critical, as it enables unleashing the potential of cloud computing. The flexibility and reliability advantages of cloud computing are not fully disclosed without automation of lifecycle processes. The flexibility of the automation solution is directly proportional to the quality of performed lifecycle processes for the entire infrastructure. Nowadays, a lot of companies are in constant search forflexible decisions for their infrastructure for further growth and decrease the usage exploitation of resources when they have a non-use state to avoid additional financial costs. Orchestrator techniques to automate configuration, coordination, and management of computer systems and software are used to meet such infrastructure's demand. Infrastructure as a Code took a large part in automation processes from the beginning of the growing demand for Cloud Computing, but now the new era of orchestration and demand on flexibility capabilities has come, which IaC has to cover. Last decade, multiple IaC solutions appeared. Each of them has a different performance as orchestrators. Flexibility of the orchestrator is measured by configuration capabilities and workflow control of operations via internal features. Nevertheless, time and required computational resources are an important part of orchestrator performance as well. Protracted delays between lifecycle processes and extra-high computational resource demand lead to high financial costs and high service downtime. Computational resource consumption and time metrics, configuration capabilities are the core of orchestrator performance

This thesis explores how large language models can be used to generate configuration files for Terraform from natural language descriptions. Few-shot and fine-tuning paradigms are evaluated on decoder-only models of varying size, including the state-of-the-art Codex model. The generated configuration files are evaluated with regard to functional correctness on a custom dataset using Terraform, to account for the large space of functionally equivalent configuration files. Results show that the largest model Codex is very capable at generating configuration files given an English description of network infrastructure even without fine-tuning. The result could be a useful tool for engineers who know Terraform fundamentals and have experience with the cloud platforms: AWS, GCP, or Azure. A future study could fine-tune Codex for Terraform using OpenAI's API or create an open source Codex-replication by fine-tuning the GPT-3 replication OPT, which in turn can be \hbox{fine-tuned}. / Denna avhandling undersöker hur stora språkmodeller kan användas till att generera konfigurationsfiler för Terraform med hjälp av språkbeskrivningar. Både few-shot och fine-tuning paradigm utvärderas på decoder-only modeller i olika storlekar, inklusive Codex. För att ta hänsyn till konfigurationsfiler som i utseende ser olika ut men som är funktionellt ekvivalenta utvärderas konfigurationsfilerna utifrån deras funktion. Resultaten visar att Codex, som är den största modellen, har förmågan att generera konfigurationsfiler givet en engelsk beskrivning av nätverksinfrastruktur, trots att Codex inte har undergått fine-tuning. Resultatet kan vara ett användbart verktyg för ingenjörer som har grundläggande kunskap om Terraform och erfarenhet av molnplattformarna: AWS, GCP eller Azure. En framtida studie skulle kunna träna Codex för Terraform med OpenAI:s API eller skapa en Codex-kopia genom att träna GPT-3 kopian OPT som i sin tur kan bli tränad för Terraform.

The master’s thesis focuses on analysis of solution to distributed computing of metage-nomics data in cloud infrastructures. It describes specific META-pipe platform based onclient-server architecture in infrastructure of public academic cloud EGI Federated Cloud,sponsored by european project ELIXIR-EXCELERATE. Thesis is focusing especially onopen-source software like Terraform and Ansible.

Infrastructure is a common word used to express the basic equipment and structures that are needed e.g.  for a country or organisation to function properly. The same concept applies in the field of computer science, without infrastructure one would have problems operating software at scale. Provisioning and maintaining infrastructure through manual labour is a common occurrence in the "iron age" of IT. As the world is progressing towards the "cloud age" of IT, systems are decoupled from physical hardware enabling anyone who is software savvy to automate provisioning and maintenance of infrastructure. This study aims to determine how a generic environment can be created for applications that can run on Unix platforms and how that underlying infrastructure can be provisioned effectively. The results show that by utilising OS-level virtualisation, also known as "containers", one can deploy and serve any application that can use the Linux kernel in the sense that is needed. To further support realising the generic environment, hardware virtualisation was applied to provide the infrastructure needed to be able to use containers. This was done by provisioning a set of virtual machines on different cloud providers with a lightweight operating system that could support the container runtime needed. To manage these containers at scale a container orchestration tool was installed onto the cluster of virtual machines. To provision the said environment in an effective manner, the principles of infrastructure as code (IaC) were used to create a “blueprint" of the infrastructure that was desired. By using the metric mean time to environment (MTTE) it was noted that a cluster of virtual machines with a container orchestration tool installed onto it could be provisioned under 10 minutes for four different cloud providers.

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.