Cloud services and solutions have served as a shift in the computer industry and create new opportunities for users. Clouds have been described as easily usable and fluid in terms of expansion and contraction depending on the real-time needs. Although the cloud is promoted with several benefits, it is not always apparent for the users that this is the case. Understanding both the benefits and challenges that exist is substantial for a successful adoption to cloud. This master’s thesis is conducted in collaboration with Exsitec ABand aims to investigate how the adoption of the cloud service Microsoft Azure will affect the development process. Also, it aims to provide a best practice for potentially needed updated working procedures, in terms of satisfaction and learnability. The investigation was performed through interviews and the System Usability Scale, to assess how the end users experienced development in a cloud environment. The thesis revealed that the Azure portal has low overall usability, but that there also exists an inconsistency of that perception. Two major factors that contributed to the satisfaction and learnability was the lack of documentation and that the Azure portal was considered hard to master. The SUS score revealed that the mean value was below an acceptable level, and thus changes in the company’s working procedures need to be implemented. Internal documentation regarding how the company should use both cloud in general, as well as the portal in particular, are required in order to increase the learnability and subjective satisfaction.

Manufacturers can develop next-generation production and service for their customers by the data gathered and analyzed from customers’ usage conditions. In this research, the operating condition of articular haulers is collected and analyzed through machine learning algorithms to predict the type of operational topographies and road surface. To achieve that, elevation data and satellite images, which were gathered from Microsoft Azure Maps, are used as data sources to identify the topography and road surface on which machines operated. In the end, two machine learning models are trained with machines’ inclination records and road roughness records, respectively, to classify the topography and road surface. For the topography classifier, the topography is categorized into four terrain labels, including "Low Hills", "Mountains", "Plains", and "Tablelands & High Hills". The road surface is classified into "Paved" and "Unpaved". A Convolutional Neural Network (CNN) image classification model is built for labeling satellite images instead of labeling manually. The results indicate that the prediction for topography labels "Plains" and "Tablelands & High Hills" has superior performance, which accounts for the majority of the raw dataset; on the contrary, the road surface classifier still needs further improvement in the future. In addition, an analysis and discussion regarding the imbalanced dataset are included, and it shows the limited effect on an extremely imbalanced dataset. Finally, the conclusion and future work are given.

The usage of cloud computing in order to move away from local servers and infrastructure have grown enormously the last decade. The ability to quickly scale capacity of servers and their resources at once when needed is something that can both be a price saver for companies and help them deliver high end products that will function correctly at all times even under heavy load to their customers. To meet todays challenges, one of the strategic directions of Attentec, a software company located in Linköping, is to examine the world of cloud computing in order to deliver robust and scalable applications to their customers. This thesis investigates the usage of cloud services in order to deploy scalable applications which can adapt to usage peaks within minutes.

In today’s computer environments the systems are getting bigger and more complex for each day that passes by. The motivating factor for this is that the customer wants to achieve more and more with their computer systems than before. The only way to really solve this task is to use even more APIs (Application program interfaces) in their systems.     When using more APIs in a system, there is a chance that the specific system provides the same type of API twice, which of course, is a waste of storage and resources. In addition, the more APIs a system contains, the bigger the risk is for mismanagement of these APIs. In the worst case, this can result in security breaches or data leaks.  This thesis investigates specific APIs provided for a customer of Sigma IT Consulting. The aim is to evaluate and organize the APIs according to their usability criteria. The main focus of the evaluation is the available documentation which will be evaluated by a questionnaire survey distributed to senior software developers at Sigma IT Consulting in Växjö. Conclusions will then be drawn depending on the result from the survey, and we can then see if Azure API management (which is a service to make a system more user – friendly) is accurate in its way of organizing with the API usability as the main focus! Unfortunately, Azure API management did not have any possibility what so ever to customize the API placement in a system, and the only way the APIs are organized is in alphabetical order. Therefore, a prototype with even more sorting functionality than Azure API management will also be presented in this thesis.

Tidigare forskning har påvisat brister i olika former av autentiseringsprocesser som leder till autentiseringsattacker. Målet med vår studie är att presentera ett antal riktlinjer som företag och privatpersoner kan följa för att minimera risken för autentiseringsattacker. Metoderna som användes för att komma fram till dessa riktlinjer var kvalitativa där en praktisk observationsstudie, en litteraturstudie samt en enkätundersökning låg till grund för vår insamlade data. Resultatet av studien pekar på att Google Cloud Platform, Amazon Web Services och Microsoft Azure alla har en stark autentiseringsprocess i jämförelse med kritik från tidigare forskning. Enkätundersökningen visade dessutom att olika former av alternativ autentisering, såsom Two Factor Authentication (2FA) och Multi Factor Authentication (MFA), rekommenderas för ett starkt försvar mot autentiseringsattacker.Uppsatsens resultat pekar även på att användarens egenansvar i autentiseringsprocessen är av stor vikt för att minimera risken för autentiseringsattacker. Säkra lösenord bör konstrueras och frekvent bytas ut. Även alternativ autentisering och begränsning av användarens tillgång till känslig information bör tillämpas. / Previous research has shown deficiencies in various forms of authentication processes that lead to authentication attacks. The goal of our study is to present a number of guidelines that businesses and individuals can follow to minimize the risk of authentication attacks. The methods used to reach these guidelines were qualitative. They consisted of a practical observational study, a literature review and a survey, which formed the basis of our collected data. The results of the study indicate that Google Cloud Platform, Amazon Web Services and Microsoft Azure all have a strong authentication process in comparison with the criticism of previous research. The survey also showed that different forms of authentication methods, such as the Two Factor Authentication (2FA) and Multi Factor Authentication (MFA), are recommended for a strong defense against authentication attacks.The thesis’ results also points to the user’s own responsibility in the authentication process are essential to minimize the risk of authentication attacks. Secure passwords should be designed and frequently replaced. Alternative authentication and restricted access to sensitive information for the user should also be applied.

This report compares the performance of container orchestration architecture and serverless computing platforms within cloud computing. The focus is on their application in managing real-time communications for electric vehicle(EV) charging systems using the Open Charge Point Protocol (OCPP). With the growing demand for efficient and scalable cloud solutions, especially in sectors using Internet of Things (IoT) and real-time communication technologies, this study investigates how different architectures handle high-load scenarios and real-time data transmission. Through systematic load testing of Kubernetes (for container orchestration) and Azure Functions (for serverless computing), the report measures and analyzes response times, throughput, and error rates at various demand levels. The findings indicate that while Kubernetes performs robustly under consistent loads, Azure Functionsexcel in managing dynamic, high-load conditions, showcasing superior scalability and efficiency. A controlled experiment method ensures a precise and objective assessment of performance differences. The report concludes by proposing a hybrid model that leverages the strengths of both architectures to optimize cloud resource utilization and performance.

The expanding adoption of cloud computing services by businesses has transformed IT infrastructure and data management in the computing space. Cloud computing offers advantages such as availability, scalability, and cost-effectiveness, making it a favored choice for businesses of all sizes. The aim of this thesis is to compare private and public cloud computing services in terms of pricing and implementation effort as well as comparing the cloud providers to each other. The top three cloud providers that will be examined are Google GCP, Microsoft Azure, and Amazon AWS. The study examines different pricing models and evaluates their effectiveness in different business scenarios. In addition, the thesis also discusses the challenges associated with building and maintaining private infrastructure and the deployment of applications to cloud computing service are examined. The research methodology involves data collection, analysis, and a case study of developing and deploying a ticketing system application on different cloud platforms. The ticket system helps to provide a realistic example and investigation of the cloud providers. The findings will help companies make informed decisions regarding the selection of the most appropriate cloud computing service based on pricing models and implementation efforts. The thesis provides valuable information on private and public cloud computing and recommends appropriate pricing models for different scenarios. This study adds to existing knowledge by analyzing current pricing models and deployment concepts in cloud computing. The thesis does not propose new solutions but follows a structured format compiling information on private, and public cloud computing and a comprehensive review of cloud computing pricing models and marketing efforts. / Den växande adoptionen av molntjänster inom företag har förändrat IT-infrastrukturen och datahanteringen inom datorområdet. Molntjänster erbjuder fördelar såsom tillgänglighet, skalbarhet och kostnadseffektivitet, vilket gör det till ett populärt val för företag i alla storlekar. Syftet med denna avhandling är att jämföra privata och offentliga molntjänster med avseende på prissättning och implementeringsinsatser samt att jämföra molnleverantörerna med varandra. De tre främsta molnleverantörerna som kommer att undersökas är Google GCP, Microsoft Azure och Amazon AWS. Studien undersöker olika prismodeller och utvärderar deras effektivitet i olika affärsscenarier. Dessutom diskuterar avhandlingen också utmaningarna med att bygga och underhålla privat infrastruktur samt implementeringen av applikationer till molntjänster. Forskningsmetodologin omfattar datainsamling, analys och en fallstudie av utveckling och implementering av ett support system på olika molnplattformar. Supportsystemet hjälper till att ge ett realistiskt exempel och undersökning av molnleverantörerna. Resultaten kommer att hjälpa företag att fatta informerade beslut när det gäller valet av lämpligaste molntjänst baserat på prismodeller och implementeringsinsatser. Avhandlingen tillhandahåller värdefull information om privat och offentlig molntjänst och rekommenderar lämpliga prismodeller för olika scenarier. Denna studie bidrar till befintlig kunskap genom att analysera nuvarande prismodeller och implementeringskoncept inom molntjänster. Avhandlingen föreslår inga nya lösningar, men följer en strukturerad format genom att sammanställa information om privat och offentlig molntjänst samt en omfattande översikt av prismodeller och marknadsinsatser inom molntjänster.

<p dir="ltr">The purpose of this qualitative case study was to describe how multi-cloud and cloud-based air gapped system security breaches occurred, how organizations responded, the kinds of data that were breached, and what security measures were implemented after the breach to prevent and repel future attacks. Qualitative research methods and secondary survey data were combined to answer the research questions. Due to the limited information available on successful unauthorized breaches to multi-cloud and cloud-based air gapped systems and corresponding data, the study was focused on the discovery of variables from several trustworthily sources of secondary data, including breach reports, press releases, public interviews, and news articles from the last five years and qualitative survey data. The sample included highly trained cloud professionals with air-gapped cloud experience from Amazon Web Services, Microsoft, Google and Oracle. The study utilized unstructured interviews with open-ended questions and observations to record and document data and analyze results.</p><p dir="ltr">By describing instances of multi-cloud and cloud-based air gapped system breaches in the last five years this study could add to the body of literature related to best practices for securing cloud-based data, preventing data breach on such systems, and for recovering from breach once it has occurred. This study would have significance to companies aiming to protect secure data from cyber attackers. It would also be significant to individuals who have provided their confidential data to companies who utilize such systems. In the primary data, 12 themes emerged. The themes were Air Gap Weaknesses Same as Other Systems, Misconfiguration of Cloud Settings, Insider Threat as Attack Vector, Phishing as Attack Vector, Software as Attack Vector, and Physical Media as Attack Vector, Lack of Reaction to Breaches, Better Authentication to Prevent Breaches, Communications, and Training in Response to Breach, Specific Responses to Specific Problems, Greater Separation of Risk from User End, and Greater Separation of Risk from Service End. For secondary data, AWS had four themes, Microsoft Azure had two, and both Google Cloud and Oracle had three.</p>