The aim of this study has been to determine whether Microsoft Azure Service Bus has been able to match the needs and demands of Sogeti and its clients during a transition to a distributed and service-oriented system, as well as investi- gate possible gains that can be attained from such a transition. The study has also critically examined the potential problems and difficulties that may arise in such a scenario. The study has been accomplished using a literature study as well as the development of a Proof-of-Concept model to simulate such a system and to make calculations and surveys of and within one. The survey has shown that the Azure Service Bus does match the pre-defined requirements and requisites, as well as demonstrated how safe communication can be guaranteed using this particular service bus. The study concludes with an analysis of the study as a whole and the results thereof in particular, as well as proposes measures for further research on the subject. Finally, recommendations are made on how the client should proceed based on what is found in the study. / Målet med denna studie har varit att utröna huruvida Microsoft Azure Service Bus har kunnat matcha de behov och krav som Sogeti och dess klienter har på en sådan vid en övergång till ett distribuerat tjänsteorienterat system, samt undersöka vilka eventuella vinster som finns att göra vid en sådan övergång. Studien har också kritiskt granskat de problem och svårigheter som kan komma att uppstå. Undersökningen har gjorts med hjälp av en litteraturstudie samt en utveckling av en Proof-of-Concept-modell för att simulera ett sådant system och för att kunna göra beräkningar och undersökningar av och inom ett sådant. Undersökningen har visat att Azure Service Bus matchar de krav och önskemål som på förhand ställts, samt visat på hur säker kommunikation kan garanteras med hjälp av denna servicebuss. Undersökningen avslutas med en analys av studien som helhet och resultatet i synnerhet, samt föreslår åtgärder vid vidare forskning inom ämnet. Avslutningsvis lämnas rekommendationer på hur uppdragsgivaren ska gå tillväga baserat på vad som i studien framkommer.

With the development of the Internet-of-Things, more and more IoT platforms come up with different structures and characteristics. Making balance of their advantages and disadvantages, we should choose the suitable platform in differ- ent scenarios. For this project, I make comparison of a cloud-based centralized platform, Microsoft Azure IoT hub and a fully distributed platform, Sensi- bleThings. Quantitative comparison is made for performance by 2 scenarios, messages sending speed adds up, devices lie in different location. General com- parison is made for security, utilization and the storage. Finally I draw the con- clusion that SensibleThings performs more stable when a lot of messages push- es to the platform. Microsoft Azure has better geographic expansion. For gener- al comparison, Microsoft Azure IoT hub has better security. The requirement of local device for Microsoft Azure IoT hub is lower than SensibleThings. The SensibleThings are open source and free while Microsoft Azure follow the con- cept “pay as you go” with many throttling limitations for different editions. Microsoft is more user-friendly.

Presenta las herramientas de Microsoft Azure aplicadas a entornos como prevención de riesgos y fraudes, reconocimiento facial y de lenguaje, análisis de mercados financieros o de cadenas genéticas, etc.

This master’s thesis deals with design of the monitoring solution for the Power BI service. The thesis is divided into theoretical, analytical and design sections. In the theoretical part describes the theoretical fundamentals, used technologies and analytical tools. The analytical part analyzes the company Intelligent Technologies, competitive solutions and data sources for the design part. The design part proposes its own solution for monitoring the Power BI service, including the costs and benefits of the proposed solution.

Portability of the Internet of Things Solutions is important in today’s society. This is due to the rapid growth of smart devices. More and more companies are choosing to use cloud services for data storage and processing. Halmstad Stadsnät AB is developing and communicating the IoT-platform and using the current state of Nokia IMPACT. The problem with IMPACT is that it does not fully support End-toend solutions. Therefore, Halmstad Stadsnät AB explores other possibilities for IoT-solutions when it comes to software. Microsoft AZURE is a platform that Halmstad Kommun uses for IT-solutions. The overall goal of this project is to test the portability of Microsoft AZURE’s IoT-solutions on IMPACTplatforms. The methods used have created an End-to-end solution for AZURE and then step by step test to transfer it to IMPACT. The project concludes that portability between these two platforms is good, but it requires certain adjustments when transferring / Portabilitet av internet of things lösningar är i dagens samhälle nödvändig. Detta pågrund av den snabba tillväxten av bärbara datorenheter. Allt fler företag väljer att använda molntjänster för lagring och bearbetning av data. Halmstad Kommun håller på att utveckla en kommuntäckande IoT-plattform och använder sig i dagsläget av Nokia IMPACT. Problemet med IMPACT är att den inte har fullt stöd för End-to-end lösningar. Därför undersöker Stadsnätet andra möjligheter för IoT-lösning när det kommer till mjukvara. Microsoft AZURE är en plattform som Halmstad kommun använder för IT lösningar. De övergripande målet med det här projektet är att testa portabiliteten för Microsoft AZUREs IoTlösnignar på IMPACT-plattformen. De metoder som använts har varit att skapa en End-to-end lösning för AZURE och sedan steg för steg testa att överföra den till IMPACT. Slutsatsen av projektet är att portabilitet mellan dessa två plattformar är god, men dock krävs vissa åtgärder vid överföring.

Forecasting has its application in various domains as the decision-makers are provided with a more predictable and reliable estimate of events that are yet to occur. Typically, a user would invest in licensed software or subscribe to a monthly or yearly plan in order to make such forecasts. The framework presented here differs from conventional software in forecasting, as it allows any interested party to use the proposed services on a pay-per-use basis so that they can avoid investing heavily in the required infrastructure. The Framework-as-a-Service (FaaS) presented here uses Windows Communication Foundation (WCF) to implement Service-Oriented Architecture (SOA). For forecasting, collection of data, its analysis and forecasting responsibilities lies with users, who have to put together other tools or software in order to produce a forecast. FaaS offers each of these responsibilities as a service, namely, External Data Collection Framework (EDCF), Internal Data Retrieval Framework (IDRF) and Forecast Generation Framework (FGF). FaaS Controller, being a composite service based on the above three, is responsible for coordinating activities between them. These services are accessible through Economic Endpoint (EE) or Technical Endpoint (TE) that can be used by a remote client in order to obtain cost or perform a forecast, respectively. The use of Cloud Computing makes these services available over the network to be used as software to forecast energy for solar or wind resources. These services can also be used as a platform to create new services by merging existing functionality with new service features for forecasting. Eventually, this can lead to faster development of newer services where a user can choose which services to use and pay for, presenting the use of FaaS as Platform-as-a-Service (PaaS) in forecasting. / Master of Science

Steel production companies must utilize new technologies and innovations to stay ahead of a highly competitive market. Recently, there has been a focus on Industry 4.0, which involves the digitalization of production to integrate with newer technologies such as cloud solutions and the Internet of Things (IoT). This results in a greater understanding of processes and data gathered in production, laying the foundation for potential machine learning (ML) implementations. ML models can improve process quality, reduce energy usage to produce more environmentally friendly products, and gain competitive advantages. Implementing several ML models in production can be difficult, as it involves dealing with different datasets and algorithms, moving models into production, and post-deployment maintenance. If these tasks are kept manually, the workload quickly becomes too large to handle effectively. This is why machine learning operations (MLOps) has recently been a popular topic. Automating parts of the ML workflow enables these systems to scale effectively as the number of models increases. This thesis aims to investigate how implementing MLOps practices can help an organization increase its use of ML systems. To do this, an MLOps framework is implemented using Microsoft Azure services together with a dataset from the stakeholder Uddeholm AB. The resulting workflow consists of automated pipelines for data pre-processing, training, and deployment of an ML model, contributing to establishing a scalable ML framework. Automating the majority of the workflow greatly eases the workload for managing the lifecycle of ML models.

The majority of cloud storage platforms rely on a centralized structure, with the most popular being Microsoft Azure. Centralization causes consumers to rely on the provider to maintain accessibility and security of data. However, platforms such as Storj.io are based on a decentralized structure. To become decentralized, Storj.io uses blockchain technology in a means to create an automated consensus mechanism between the entities storing the data. There is however little research regarding performance and security issues on a decentralized platform based on blockchain technology. The purpose of this study is to identify the beneficial and non-beneficial aspects of using blockchain-based decentralized cloud storage as a substitute for centralized ones. The study focuses on performance and security. A comparative case study has been executed, consisting of an experiment and literature study. Quantitative data from an experiment was used in a hypothesis test to determine whether there were any performance differences between Microsoft Azure Blob Storage and Storj.io. A literature study generating qualitative data was then made to identify differences in security measures and from that also discuss potential security risks on a service like Storj.io. This study found that the performance of Storj.io was lower than Microsoft Azure’s Blob Storage. Causes of these results were identified to be due to the many more steps during resource allocation in Storj.io, compared to Blob Storage. Security risks identified in Storj.io through the literature study were generally connected to the consensus mechanism. However, research shows that it is very unlikely for the consensus mechanism to be compromised. Because Microsoft Azure’s service does not use a blockchain, these risks do not exist. For secure data transfer to Azure’s service, consumers have to implement encryption manually client-side. Therefore, this study could not conclude whether Storj.io is a safe alternative because a consumer using the Microsoft Azure service is responsible for implementing security measures. Conclusions drawn from this study are intended to act as new knowledge in the field of blockchain-based decentralized cloud storage. It is an outset to decide whether to use centralized cloud storage or blockchain-based decentralized cloud storage from a performance and security perspective. / Majoriteten av datalagringsmolntjänsterna är centraliserade, varav Microsoft Azure står som den mest använda molntjänsten. Centralisering innebär att konsumenten behöver lita på att värdföretaget hanterar tillgänglighet och säkerheten av data på bästa möjliga sätt. I kontrast mot en centraliserad molnplattform finns Storj.io som är en decentraliserad molnlagringstjänst. För att åstadkomma decentralisering använder sig Storj.io av blockkedjan som används för att uppnå den autonoma konsensusmekanismen mellan noderna som lagrar data. Syftet med denna studie är att identifiera för- respektive nackdelarna med en decentraliserad blockkedjebaserad molnplattform i jämförelse mot en centraliserad molnplattform. Specifikt fokuserar studien på prestanda och säkerhet. En komparativ fallstudie har utförts med ett experiment och en litteraturstudie som datainsamlingsmetoder. Den kvantitativa datan från experimentet användes i en hypotesprövning för att identifiera om det fanns någon skillnad i prestanda mellan Microsoft Azure och Storj.io. Litteraturstudien användes i syfte för att kunna styrka skillnader om säkerhetsåtgärder och säkerhetsrisker mellan molnplattformarna. Resultatet av denna studie visar att prestandan för Storj.io är lägre än Microsoft Azures molnplattform. De identifierade faktorerna som orsakade resultatet anses vara på grund av de flertal steg som krävs vid resursallokering för Storj.io. De säkerhetsrisker som uppstår hos Storj.io kom till i samband med konsensusmekanismen. För att en säkerhetsrisk skall uppstå mot konsensusmekanismen behöver det decentraliserade nätverket hotas med majoritet. Eftersom Microsoft Azure inte använder sig av blockkedjan uppstår inte dessa typer av säkerhetsrisker. För dataöverföring till Azures datalagringstjänst behöver konsumenten själv säkerställa en krypterad kommunikationskanal. I Storj.ios fall sköts alla typer av säkerhetsåtgärder automatiskt vilket eliminerar risken för säkerhetsattacker vid överföringar. Sammanfattningsvis tyder denna studie på att Storj.io inte är ett optimalt val vid prioritering av prestanda. Eftersom konsumenten som använder Microsoft Azures tjänst ansvarar för säkerhetsåtgärder drogs ingen direkt slutsats huruvida Storj.io är ett säkert substitut. Studien visar på att det existerar konensusrisker med en tjänst som Storj.io och det är upp till envar konsument att förlita sig på att dessa inte uppstår. De slutsatser som har dragits från denna studie är avsedda som ny kunskap inom fältet som berör decentraliserade molnplattformar baserade på blockkedjan. Studien kan användas som en utgångspunkt för val mellan en centraliserad och decentraliserad molntjänst baserad på blockkedjan med prioritet för prestanda och säkerhet.

When assessing network systems, security has always been one of the priorities.Cyber threat modelling is one of the most suitable methods. From a startingpoint to each valuable asset, the simulation can enable the users to explore certainsecurity weaknesses alongside the attack path. In the end, the time to compromiseshows the security level of the whole system.In principle, most cyber threat models can be built and simulated by attack graphswhere each point in the graph can stand for a certain asset in the network system.However, different systems have different infrastructures and implementations.As a result, it will be more suitable if engineers can develop a domain specificlanguage (DSL) which can be associated with a specific attack graph in order toimprove accuracy and efficiency.In this master thesis work, the final outcome is azureLang, a cyber threat modelinglanguage based on Meta Attack Language (MAL) for Microsoft Azure cloudcomputing environment. Compatible with securiCAD®, a CAD tool developedby Foreseeti AB, a threat model can be built and then be simulated. / Vid bedömning av nätverkssystem har säkerhet alltid varit en av prioriteringarna.Bland tusentals metoder är cyberhotsmodellering en av de mest lämpliga. Frånen startpunkt till varje värdefull tillgång kan simuleringen göra det möjligt föranvändare att utforska vissa säkerhetssvagheter längs attackvägen. I slutändanvisar tiden för kompromiss säkerhetsnivån för hela systemet.I princip kan de flesta cyberhotsmodeller byggas och simuleras med attackgraferdär varje punkt i diagrammet kan stå för en viss tillgång i nätverkssystemet. Menolika system har olika infrastrukturer och implementationer. Som ett resultatkommer det att vara mer lämpligt om ingenjörer kan utveckla ett domänspecifiktspråk (DSL) som kan associeras med en specifik attackgrafik för att förbättranoggrannhet och effektivitet.I det här examensarbetet är slutresultatet azureLang, ett språk för modelleringav hothot baserat på Meta Attack Language (MAL) för Microsoft Azure cloudcomputing-miljö. Kompatibel med securiCAD ®, ett CAD-verktyg utvecklat avForeseeti AB, en hotmodell kan byggas och sedan simuleras.

Major advances in machine learning have made image recognition applications, with Artificial Neural Network, blossom over the recent years. The aim of this thesis was to find a solution to recognize digits from a number sequence on an ID tag, used to identify farm animals, with the help of image recognition. A Recurrent Convolutional Neural Network solution called PPNet was proposed and tested on a data set called Animal Identification Tags. A transfer learning method was also used to test if it could help PPNet generalize and better recognize digits. PPNet was then compared against Microsoft Azures own image recognition API, to determine how PPNet compares to a general solution. PPNet, while not performing as good, still managed to achieve competitive results to the Azure API.