Today, data warehouses are used to store large amounts of data. This thesis investigates the impact of various database schema designs on query execution time within the cloud platform Azure Data Explorer. As Azure Data Explorer is a relatively new platform, limited research exists on designing database schemas within the platform. Further, the design of the database schema has a direct impact on the query execution times. The design should also align with the use case of the data warehouse. This thesis conducts a requirements analysis, determines the use case, and designs three database schemas. The three database schemas are implemented and evaluated through a performance test. Schema 1 is designed to utilize results tables from stored functions, while schema 2 utilizes sub-functions divided by different departments or products to minimize the data accessed per query. Finally, schema 3 uses the results tables from the sub-functions found in schema 2. The result from the performance tests shows that schema 3 has the best overall improvement in query execution time compared to the other designs and the original design. The findings emphasize the critical role of database schema design in influencing query performance. Additionally, a conclusion is reached that using more than one approach to enhance query performance increases the potential query performance.

In recent years, the interest and utilization of VPNs and cloud computing have surged, which has led to the development of Cloud VPNs. Cloud VPNs are often used to give employers access to company resources over a secured and trusted network, but individuals can also use them. Cloud computing offers numerous advantages, including cost efficiency, scalability, security, and reliability. Companies and individuals widely use it to streamline operations, reduce expenditures, and leverage technologies without the need to maintain on-premise infrastructure. Cloud-based solutions impose specific requirements on technologies designed to operate in the cloud, which include security, efficient resource management, and high network availability and performance. In this thesis, we will analyze and evaluate the network performance impact that Amazon Web Services (AWS) and Microsoft Azure, two of the market-leading cloud platforms, have on the VPN services OpenVPN and WireGuard while considering associated operational costs and user complexity. The network performance impact is evaluated through measuring the throughput, latency, jitter, and packet loss. We have performed experiments divided into three setups, each simulating different traffic patterns, and VPN usage scenarios. Throughout these experiments, we observed and documented the user complexity related to setup, installation, and configuration processes. Our findings indicate that Azure has the best overall network throughput across all setups, fewer retransmissions, and fewer packet losses. Conversely, AWS exhibits lower latency and jitter. Additionally, our assessment of operational costs and user complexity reveals that Azure offers lower associated costs but a higher user complexity. Furthermore, our experiments identified that WireGuard, when paired with Azure, offers the best VPN solution.

I takt med att datakvantiteter växer och kraven på skalbarhet och tillgänglighet inom molntjänster växer, framhävs behovet av undersökningar kring dess prestanda och kostnadseffektivitet.  Dessa analyser är avgörande för att optimera tjänster och bistå företag med värdefulla rekommendationer för att fatta välgrundade beslut om datalagring i molnet. Detta examensarbete undersöker kostnad samt prestanda hos relationella och icke-relationella datalagringslösningar implementerade på Microsoft Azure och Google Cloud Platform. Verktyget Hyperfine används för att mäta latens och tjänsternas kostnadseffektivitet beräknas baserat på detta resultat samt dess beräknade månadskostnader. Studiens resultat indikerar att för de utvärderade relationella databastjänsterna uppvisar Azure SQL Database initialt en låg latens som sedan ökar proportionellt med datamängden, medan Google Cloud SQL visar en något högre latens vid lägre datamängder men mer konstant latens vid högre datamängder. Azure SQL visar sig vara mer kostnadseffektiv i förhållande till Google Cloud SQL, vilket gör den till ett mer fördelaktigt alternativ för företag som eftersträvar hög prestanda till lägre kostnader. Vid jämförelse mellan de två icke-relationella databastjänsterna Azure Cosmos DB och Google Cloud Datastore uppvisar Azure Cosmos DB genomgående jämförelsevis lägre latens och överlägsen kostnadseffektivitet. Detta gör Azure Cosmos DB till en fördelaktig lösning för företag som prioriterar ekonomisk effektivitet i sin databashantering. / As data volumes grow and the demands for scalability and availability within cloud services increase, the need for studies on their performance and cost-effectiveness is emphasized. These analyses are crucial for optimizing services and providing businesses with valuable recommendations to make well-grounded decisions about cloud data storage. This thesis examines cost and performance for relational and non-relational data storage solutions implemented on Microsoft Azure and Google Cloud Platform. The tool Hyperfine is used to evaluate latency and the cloud services cost efficiency is calculated using this result as well as their monthly cost. The study's results regarding relational data storage indicate that Azure SQL Database initially exhibits low latency, which then increases proportionally with the data volume, while Google Cloud SQL shows slightly higher latency at smaller data volumes but more consistent latency with more data. Azure SQL Database is more cost-effective, making it a more favorable option than Google Cloud SQL for companies seeking high performance at lower costs. Regarding evaluated services for non-relational data storage Azure Cosmos DB consistently demonstrates lower latency and superior cost efficiency compared to Google Cloud Datastore, making it the preferred solution for companies prioritizing economic efficiency in their database management.

This thesis describes how a solution can be built to detect human flu-like symptoms. Flu-like symptoms are important to detect to prevent Covid-19 [6]. As people are returning to work there is a need for a simple way of detecting flu-like symptoms to prevent the spread of Covid-19. Other than a solution, this thesis concluded how human flu-like symptoms can be detected, with cameras specifically. This is to know what symptoms are most likely to work for a prototype. The technique of cameras and thermal cameras made this project possible as well as the technique of a single-board computer. The technique of cloud-based services is also an important part of this project. This project has resulted in a novel prototype using a single-board computer, cameras, and various cloud-based services to detect and inform a person if he or she has a human flu-like symptom.

Cloud Computing is becoming increasingly popular, with large amounts of corporations revenue coming in from various cloud solutions offered to customers. When it comes to choosing a solution, multiple options exist for the same problem from many competitors. This report focuses on the ones offered by Microsoft in their Azure platform, and compares the architectures in terms of performance and scalability.In order to determine the most suitable architecture, three offered by Azure are considered: Cloud Services (CS), Service Fabric Mesh (SFM) and Virtual Machines (VM). By developing and deploying a REST Web API to each service and performing a load test, average response times in milliseconds are measured and compared. To determine scalability, the point at which each service starts timing out requests is identified. The services are tested both by scaling up, by increasing the power of a single instance of a machine, and by scaling out, if possible, by duplicating instances of machines running in parallel.The results show that VMs fall considerably behind both CS and SFM in both performance and scalability, for a regular use case. For low amounts of requests, all services perform about the same, but as soon as the requests increase, it is clear that both SFM and CS outperform VMs. In the end, CS comes ahead both in terms of scalability and performance.Further research may be done into other platforms which offer the same service solutions, such as Amazon Web Services (AWS) and Google Cloud, or other architectures within Azure. / Molntjänster blir alltmer populära i dagens industri, där stora mängder av företagens omsättning består av tjänster erbjudna i form av molnlösningar. När det kommer till att välja en lösning finns många för samma problem, där det är upp till kunden att välja vilken som passar bäst. Denna rapport fokuserar på tjänster erbjudna av Microsofts Azure plattform, i en jämförelse av arkitekturer som belastningstestas för att mäta prestanda och skalbarhet.För att avgöra vilken arkitektur som är optimalast mäts tre olika tjänster erbjudna i Azure: Cloud Services (CS), Service Fabric Mesh (SFM) och Virtual Machines (VM). Detta görs genom att utveckla och deploya ett REST Web API som är simulerat med användare, där prestanda mäts genom att ta medelresponstiden i millisekunder per anrop. För att avgöra skalbarhet identifieras en punkt där tjänsten inte längre klarar av antalet inkommande anrop och börjar returnera felkoder. Maskinerna för varje tjänst testas både genom att skala upp, genom att förstärka en maskin, men även genom att skala ut, där det skapas flera instanser av samma maskin.Resultatet visar att Virtual Machines hamnar betydligt efter både CS och SFM i både prestanda och skalbarhet för ett vanligt användarfall. För låga mängder anrop ligger samtliga tjänster väldigt lika, men så fort anropen börjar öka så märks det tydligt att SFM och CS presterar bättre än Virtual Machines. I slutändan ligger CS i framkant, både i form av prestanda och skalbarhet.Vidare undersökning kan göras för de olika plattformarna erbjudna av konkurrenter, så som Amazon Web Services (AWS) och Google Cloud, samt andra arkitekturer från Azure.

Cyber-attack simulation is a suitable method used for assessing the security ofnetwork systems. An attack simulation advances step-wise from a certain systementry-point to explore the attack paths that lead to dierent weaknesses inthe model. Each step is analyzed, and the time to compromise is calculated.Attack simulations are primarily based on attack graphs. The graphs areemployed to model attack steps where nodes can represent assets in the system,and edges can represent the attack steps. To reduce the computational cost associatedwith building an attack graph for each specic system, domain-specicattack languages, or DSL for short, are used.The nal product of this thesis work is azureLang, a probabilistic modelingand simulation language for modeling Microsoft Azure cloud infrastructure.AzureLang is a DSL which denes a generic attack logic for MicrosoftAzure systems. Using azureLang, system administrators can easily instantiatespecic-system scenarios which emulate their Microsoft Azure cloud system infrastructure.After creating the model, attack simulation can be run to assessthe security of the model. / Cyberattacksimulering är en lämplig metod som används för att bedöma säkerhetenhos nätverkssystem. En angrepsimulering går stegvis från ett visst systeminmatningspunkt för att utforska angreppsbanorna som leder till olika svagheter i modellen. Varje steg analyseras och tiden för kompromettera beräknas.Attack-simuleringar baseras huvudsakligen på attackgrafer. Graferna används för att modellera angreppssteg där noder kan representera tillgångar i systemet, och kanterna kan representera attackenstegen. För att minska kostnaden för att skapa attackgrafer för varje specifikt system används domänspecifika språk eller DSL förkortat.Den slutliga produkten av detta examensarbete är azureLang, ett probabilistisk hotmodelleringsoch attacksimuleringsspråk för analys av Microsoft Azure Cloud Infrastructure. AzureLang är en DSL som definierar en generisk attacklogik för Microsoft Azure-system. Med hjälp av azureLang kan systemadministratörer enkelt ordna specifika systemscenarier som efterliknar deras Microsoft Azure cloudsystem infrastruktur. Efter att ha skapat modellen kan attack simu-lering köras för att bedöma modellens säkerhet.

At Halmstad University a project was developed to provide recommendations forupgrading and downgrading the cloud resource app service plan based on the customersusage over the last 30 days. In today’s day and age, cloud resources and services are oftenquite expensive and offers a variety of different plans which can make it overwhelmingfor the customer to easily choose which tier they need for their plan. The result of thisscript indicate that the cloud users should consider changing subscription tier based onhow the historical data of their usage of the plan has looked like during the last 30 days.The proposed algorithm suggests an upgrade of a tier if the plan is overutilized andsuggest a downgrade of a tier if the plan is underutilized. The developed PowerShell codeuses the First-Fit and the Rule-based algorithmic approach from the related workresearched in the paper. The result found was that the code was able to give suitablerecommendations to scale up and down tiers for plans which were under and overutilizedbased on the percentual utilization rules set up and Legacy/DEV SKU mapping. Theresults obtained showed that the suggested plan can reduce costs by up to 30% and giveroughly 438.2% more performance per $USD spent. / Vid Högskolan i Halmstad utvecklades ett projekt för att ge förslag på uppgraderingaroch nedgraderingar av molnresursern app service plan baserat på användarens senaste 30dagars användning. Då dagens molnresurser och tjänster ofta är dyra och erbjuder ettöverflöd av planer, kan det vara förvirrande för användare att välja rätt nivå för sinabehov. Projektet föreslår att användarna ska överväga att byta plan beroende på hur denhistoriska datan har sett ut för planens användning, där en uppgradering rekommenderasom tjänsten är överanvänd och en nedgradering om planen är underanvänd. Denutvecklade PowerShell koden använder sig av First-fit och det regelbaserade algoritmtypen som utvecklades med inspiration från litteraturstudien. Resultatet av projektetindikerar att koden kunde ge optimala upp och ned skalnings rekommendationer beroendepå de olika procentuella trösklarna satta samt mappningen av Legacy och utvecklingstiers. Analyseringen av resultatet pekar på att det går att spara runt 30% på app serviceplan kostnaderna samt att app service planerna får 438,2% mer prestanda per spenderad$USD i jämförelse med nuvarande planen.

The field of Artificial Intelligence (AI) has seen much development in recent years. This is mostly because of new technologies and computers becoming faster. With actors like Microsoft releasing software to reduce the complexity of the development of AI, the barrier to entry is now lower than ever. Due to this lower barrier, one area that has gained much traction is chat bots, being able to act as first line support for companies. While the technology is evolving fast and updates to Microsoft’s tools are released at an impressive rate, it appears to be difficult for other areas like documentation and ease of use to keep up with this pace. This thesis will explore some tools from Microsoft used in AI development. Areas covered are the Cognitive Services with a focus on speech, the Microsoft Azure Bot Service, the QnA Maker and the bot framework’s web-based client. These tools will be evaluated in the context of a chat bot, taking into account their functionality and development. The result is a chat bot that uses a knowledge base for data storage & answer logic, and a web interface for chat functionalities using the bot framework web-client.

The technology of “augmented reality” has existed in an experimental stage since the early 1990s, but it is not until around year 2010 that commercial use in the form of both hardware and software became available, for example, when Pokémon GO and Google Glass were announced. The aim of this thesis is to develop a mobile application that utilizes the technique of augmented reality to gain an insight into the technique’s potential, as well as the various tools available in this type of development. The project resulted in an augmented reality application, written in the program language Kotlin for the mobile operating system Android. The main use of the application includes group activities such as tourism, as the main functionality is to visualize other users' location via the mobile camera using the tools ARCore and Azure’s database Cosmos DB. Even though the technology is relatively new, the work has progressed without major difficulties, and even with basic programming knowledge it is possible to do software development in this area.

In this thesis, a scalable, highly available and reactive RabbitMQ cluster is implemented on Amazon Web Services (AWS) and Microsoft Azure. An alternative solution was created on AWS using the CloudFormation service. These solutions are performance tested using the RabbitMQ PerfTest tool by simulating high loads with varied parameters. The test results are used to analyze the throughput and price-performance ratio for a chosen set of instances on the respective cloud platforms. How performance changes between instance family types and cloud platforms is tested and discussed. Additional conclusions are presented regarding the general performance differences in infrastructure between AWS and Microsoft Azure.