The purpose of this study is to compare the software deployment tools Ansible, Chef and SaltStackregarding deployment time and their respective resource utilization, and the findings of this studyare also compared to the previous works of Benson et al. (2016), which also studied deploymenttime. However, there is no previous research which mentions resource utilization, which is equallyimportant. The study consists of an experiment performed in one of the laboratory rooms at theUniversity of Skövde where all three software deployment tools are configured to deploy a setamount of packages to three hosts each.By measuring deployment time with the most stable releases (as of 2017-04-22) for each softwaredeployment tool, as well as resource utilization for each host and server, this study may assist systemadministrators to make more informed decisions when deciding which application to use to managetheir computers and infrastructure.The results of the study show that Chef is the fastest software deployment tool in terms ofdeployment time. Chef is also shown to be the most optimized application, as its usage of resourcesis better than both Ansible and SaltStack. / Syftet med denna studie är att studera och jämföra fjärrinstallationsprogrammen Ansible, Chef ochSaltStack gällande den tid det att installera en mängd program på ett antal klienter, och derasrespektive resursutnyttjande. Resultaten från denna studie jämförs även med tidigare studier avBenson et al. (2016), som också studerat tidsåtgången för Ansible, Chef och SaltStack. Det finnsemellertid ingen tidigare forskning som nämner resursutnyttjande, vilket är lika viktigt. Studienbestår av ett experiment som utförs i ett av laboratorierna vid Högskolan i Skövde där alla treprogram konfigureras och användes för att installera en viss mängd paket till tre klientdatorervardera.Genom att mäta tiden det tar för varje program med de senaste stabila utgåvorna (2017-04-22),samt resursutnyttjandet för varje klientdator och server, kan systemadministratörer läsa dennastudie för att fatta mer informerade beslut när de bestämmer vilken applikation som ska användasför att hantera deras datorer och infrastruktur.Resultaten av studien visar att Chef är det snabbaste fjärrinstallationsprogrammet för att installerapaket på klienter. Chef visar sig också vara den mest optimerade applikationen, eftersom dessresursutnyttjande är bättre än både Ansible och SaltStack.

Denna rapport har skrivits under ett examensarbete på Linköpings Universitet för att ta reda på vilket program som är mest tidseffektivt att använda för hantering av datorkonfigurationer. De konfigurationshanterarna som analyserades i detta arbete är Ansible, Chef och Puppet som är tre av de ledande programmen inom ämnet. Motiveringen till arbetet är att när komplexiteten hos infrastrukturen ökar, så är det viktigt att hitta en konfigurationshanterare som verkställer konfigurationen på systemen tillräckligt snabbt för att möta den komplexa infrastrukturens behov. Jämförelsen görs baserat på hur lång tid det tar för varje program att genomföra olika typer av konfigurationer, med olika uppsättningar av antalet system som konfigureras, och från denna data görs slutsatser om programmens tidseffektivitet, spridning och skalning.

The master's thesis is focused on the design and implementation of a tool for generating configuration named Ansible. The result of using this tool is generated configuration, which contains random values chosen according to specified parameters and it was deployed on a virtual testing infrastructure. The theoretical part describes approaches of network automation in the process of deploying and configuration of network devices called Infrastructure as code. It also describes programme Ansible, which will be using the output of the implemented tool. The practical part of the thesis is focused on designing the functionality and internal structure of the tool, implementation of the tool and testing implemented tool as well as generated configuration.

Automating the configuration of network devices contributes to faster configuration and helps increase consistency compared with manual configuration. In this thesis, we compare the automation tools Ansible, Puppet, and SaltStack and evaluate their performance when configuring network devices. To evaluate the automation tools this thesis consists of a comprehensive overview and a set of performance experiments. The comprehensive overview focuses on comparing the automation tools and their capabilities whilst the set of performance experiments focuses on comparing the performance of the automation tools. The comprehensive overview reveals that all automation tools we investigate can achieve automated network configuration. However, the approach to configure network devices varies between the automation tools. For the configuration of network devices Ansible is more capable than Puppet and SaltStack. The lack of documentation and support for Puppet and SaltStack leads to an increase in usage complexity. The performance experiments reveal that Ansible and SaltStack have a better performance than Puppet for a lower number of configuration changes. However, with an increasing number of configuration changes, Ansible shows a noticeable difference whilst Puppet and SaltStack do not.

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

Cloud computing offers higher reliability and lower up-front IT costs than traditional computing environments and is a great way to dynamically scale both resources and capabilities. For further efficiency and consistency, cloud computing tasks can also be automated with tools such as Ansible. In this thesis, we will analyze and compare the abilities of Ansible with the three leading cloud platforms, i.e. Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP). To evaluate this, we cover three different areas of automation with cloud platforms. These areas are performance, user complexity, and missing network functionalities. The performance was evaluated through experiments that revealed a big gap between the platforms, where AWS was the clear winner in all scenarios. Microsoft Azure was slightly faster than GCP when a low number of virtual machines were created but GCP scaled better than Azure when more virtual machines were created. The user complexity was evaluated on the setup process and the creation of playbooks where Azure was the clear winner in both areas. AWS and GCP had similar setup processes, but AWS takes second place through its superior documentation for the creation of playbooks. All three platforms had missing network functionalities, but the majority of the missing functionalities were not relevant as they were not related to deployment which is the main usage of an automation tool like Ansible. However, some deployment functions were missing, for example, the firewall function for AWS was missing in Ansible.

This report discusses network automation primarily with Ansible. Ansible is a software from Red Hat that can be used for network automation. The report also goes through YAML which is a standardized way of exchanging data, Jinja2 that is a templating language, Python as well as the security with Ansible. The report also goes through why network automation is needed as well as how much time might be saved with Ansible. Ansible ships with modules for Cisco IOS such as ios_config and ios_command and for Cisco ASA asa_config, asa_command and asa_acl as well as many other modules for Arista, Juniper and for other vendors. Ansible can use new APIs by creating new modules for handling that particular API, which means that the only change needed in the playbooks is to change the module name. Ansible can handle NETCONF API using the netconf_config module or various Juniper modules. Ansible is used in this report to perform certain tasks such as to adding VLAN's, close ports on ASA's, audit network devices configuration as well as to create network diagram using the information from CDP. Ansible can be made as secure as manually doing the tasks except that Ansible can do it faster and more consistently.  For connecting to normal Linux servers Ansible uses OpenSSH which is a default SSH client on most Linux systems and for connecting to network devices it uses Paramiko. The security in Ansible depends on SSH and may or may not have passwords stored locally, Ansible can be as secure as the administrator wants it to be such as using RSA key-pair to authenticate, using vault encrypted credentials or asking the administrator about which username and password to use. Using Ansible network automation can save time, the amount saved depends on what is being done, how many devices it is doing it on as well as how the playbook is written. / Rapporten behandlar nätverksautomation primärt i Ansible. Ansible är en mjukvara från Red Hat som kan användas för nätverksautomering. Rapporten går igenom YAML som är ett sätt att standardisera överförning av data, Jinja2 som är ett mallspråk, Python samt säkerheten i Ansible. Rapporten går dessutom igenom varför vi ens vill ha nätverksautomation och hur mycket tid som möjligtvis kan sparas. Ansible kommer med moduler för Cisco IOS som exempelvis ios_config och ios_command och för Cisco ASA finns moduler så som asa_config, asa_command och asa_acl. För andra tillverkare så finns det moduler för Arista, Juniper och för andra leverantörer. Om en ny API kommer ut för en ny enhet så kan en ny Ansible modul skapas som använder denna, vilket betyder att Ansible playbooks kan då använda sig av de nya modulerna med samma struktur som tidigare. Ansible kan hantera NETCONF API med hjälp av netconf_config modulen och av flertalet Juniper moduler. Ansible kan användas på ett lika säkert sätt som manuellt arbete, med undantag på att Ansible gör det snabbare och mer konsekvent. För uppkoppling till vanliga Linux-servrar så använder Ansible OpenSSH klienten som standard och mot nätverksenheter utan Python installerat så används Python biblioteket Paramiko. Ansible använder sig av SSH och kan ha lösenord sparat i playbooken, utanför i annan fil, i ett krypterat vault, fråga användaren som användarnamn och lösenord samt autentisering med hjälp av RSA nycklar. Ansible används för att skapa olika VLAN, stänga portar på en ASA, granska nätverksenhetens konfiguration gentemot vad den borde ha för konfiguration samt för att skapa nätverksdiagram baserat på informationen från CDP. Genom att använda sig av Ansible nätverksautomation så kan tid sparas, hur mycket beror helt på vad som ska göras, hur många enheter det ska göras på samt hur playbooken faktiskt är skapad.

The use of physical systems and Virtual Machines has become inefficient and expensive for creating tailored, hands-on exercises for providing cyber security training. The main purpose of this project is to directly address these issues faced in cyber security education with the help of Docker containers. Using Docker, a lightweight and automated platform was developed for creating, sharing, and managing hands-on exercises. With the help of orchestration tools, this platform provides a centralized point to monitor and control the systems and exercises with a high degree of automation. In a classroom/lab environment, this infrastructure enables instructors and students not only to share exercises but also helps create and deploy exercises more easily. By streamlining the end to end delivery and deployment of the exercises, instructors can now efficiently make use of the class/lab hours in educating the students rather than performing system administration tasks.

Tato diplomová práce popisuje úpravu průběžné integrace pro Platform tým ve společnosti Red Hat. Výsledkem práce je nástroj Metamorph, který umožní sjednocení ostatních nástrojů průběžné integrace pod týmem Platform. Teoretická část popisuje vznik, popis a přidané hodnoty průběžné integrace. Následně jsou blíže přiblíženy existující nástroje na trhu. Dále je zde popsáno použití průběžné integrace v nástroji Jenkins. V práci jsou také dopodrobna popsány existující řešení průběžné integrace ve společnosti Red Hat. Dále je zde popsán návrh a implementace výše zmíněného nástroje. V závěru jsou výsledky práce otestovány týmem z firmy Red Hat a nastíněny možnosti rozšíření.

Tato diplomová práce se zabývá problémem bezpečné distribuce software. Je navrženo zlepšení s pomocí doménového systému, který je použit pro uložení verifikačních klíčů, potřebných pro ověření integrity balíků stáhnutých pomocí správce balíků. Navíc je navržena rozšířená verze, které se zabývá zabezpečením metadat repositářů. Obě verze jsou implementovány v jazyce Python a integrovány do správce balíků dnf. Tato implementace je otestována ve virtuálním prostředí, diskutována a zhodnocena z hlediska způsobené zátěže.