The thesis deals with the use of cloud computing to solve computationally intensive task. Simulation methods are employed to analyze the operation of the proposed system before putting it into operation. Cloud computing makes the functionality of modern data centers accessible to small businesses, cloud allows scaling up and scaling down of resource usage on an as-needed basis; moreover, without high initial investments and long-term commitments. The paper examines in detail the ways to adapt the currently most popular cloud environment of Amazon's Elastic Compute Cloud to the specific requirements of custom applications and suggests ways to automate the process of creation and customization of instance images. The automation and management of running applications in cloud environment is demonstrated on the example of geographic data processing and map generation application. The simulation model for running applications in the cloud is built and exploited to verify the ability of the proposed solutions to succeed in a production deployment.

There is a large number of biosignals that can be obtained from the human body. Repositories of biosignals are used for researchers in many studies as datasets. Datasets with clinical and personal information have many regulations and they are not accessible to everyone. Platforms with acquisition, recording and visualization of data are currently used in homecare systems for monitoring patients in an effcient way. Distant monitoring allows a good and prompt communication between patients and physicians. This work describes the design and development of a platform at hardware and software level for recording, storage, visualization and analysis of biosignals on the cloud. For the platform, IoT and Cloud Computing resources were used. Open-source and free software are considered for an easy and flexible enlargement of the system.

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.

This master’s thesis evaluates automatic data annotation and machine learning predictions of iron ore pellets using tools provided by Amazon Web Services (AWS) in the cloud. The main tool in focus is Amazon SageMaker which is capable of automatic data annotation as well as building, training and deploying machine learning models quickly. Three different models was trained using SageMakers built in semantic segmentation algorithm, PSP, FCN and DeepLabV3. The dataset used for training and evaluation contains 180 images of iron ore pellets collected from LKAB’s experimental blast furnace in Luleå, Sweden. The Amazon Web Services solution for automatic annotation was shown to be of no use when annotating microscopic images of iron ore pellets. Ilastik which is an interactive learning and segmentation toolkit showed far superiority for the task at hand. Out of the three trained networks Fully-Convolutional Network (FCN) performed best looking at inference and training times, it was the quickest network to train and performed within 1% worse than the fastest in regard to inference time. The Fully-Convolutional Network had an average accuracy of 85.8% on the dataset, where both PSP & DeepLabV3 was showing similar performance. From the results in this thesis it was concluded that there are benefits of running deep neural networks as a cloud service for analysis and management ofiron ore pellets.

Preserving the performance of cloud services according to service level agreements (SLAs) is one of the most important challenges in cloud infrastructure. Since the workload is always changing incrementally or decremental, managing the cloud resources efficiently is considered an important challenge to satisfy non-functional requirements like high availability and cost. Although many common approaches like predictive autoscaling could solve this problem, it is still not so efficient because of its constraints like requiring a workload pattern as training data. Reinforcement machine learning (RL) can be considered a significant solution for this problem. Even though reinforcement learning needs some time to be stable and needs many trials to decide the value of factors like discount rate, this approach can adapt with the dynamic workload. In this  thesis, through a controlled experiment research method, we show how a model-free reinforcement algorithm like Q-learning can adapt to the dynamic workload by applying horizontal autoscaling to keep the performance of cloud services at the required level. Furthermore, the Amazon web services (AWS) platform is used to demonstrate the efficiency of the Q-learning algorithm in dealing with dynamic workload and achieving high availability.

Task of this master thesis is to create system for support of automated testing and deploying of server applications according to requirements defined by company AVG. The main target is create system for deploying and testing that automatically or manually test the server application in the cloud environment with ability of final deployment to the production environment with overall progress monitoring. Automated testing and deploying system has been split to the two parts. The first part is user interface that allows adding new applications, testing applications and deploying applications to its production environment. The second part represents the REST service which process testing and deploying tasks and  store progress to database. System was tested with sample server's applications in Amazon Web Services cloud environment. Thesis describes general testing principles cross multiple areas. Also it contains general architecture withing diagrams, which shows use cases. At the end of thesis is described testing of samples application together with results.

This diploma thesis focuses on the design, development and subsequent implementation of the web application at the company BKB Metal Plc. In that case it is the complete supply ready-made solution. The aim of this work is to develop and implement the web application into corporate environment that will serve as a support of project management and will especially provide the solution for creating visualization by using a Gantt diagram. The impetus for this project was primarily the inconvenient of the current solution, which is not support the key company requirements and as well efforts to reduce costs.

Purpose: This thesis aims to describe how to design and implement an IoT-Based digital twin framework for environmental monitoring in the indoor environment.  To fulfill the purpose of the study, the following research question is answered. How to create a digital twin solution utilizing AWS to establish interaction and convergence between the physical environment in a classroom and the virtual environment?  Method: As a research method, the research has conducted design science research (DSR). DSR is a new method, and it is an effective tool for enhancing engineering education research methods.  Results: The study describes in detail the steps required to create the framework. The framework enabled interaction and convergence between the physical and virtual environments in a particular location.  Implications: The research contributes to broadening the knowledge on using the Internet of things (IoT), digital twin (DT), and Amazon web services (AWS). The study provides future research with reference data and a framework to build upon.  Research Limitation: Due to time constraints, the study's scope and limitations are limited to the technologies that the participating company, Knowit, provides. Knowit AB is a Swedish IT consulting company that supports companies and organizations with services in digital transformation and system development. The study aims to create an AWS-based IoT framework, not improve the digital twin concept. The framework was implemented at Jönköping University. This work is also limited to temperature and light intensity as environmental parameters.

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 is a Masters student Final Dissertation / In this project, two tests were performed. On the first test, Mininet-WiFi was used to simulate a Software Defined Network to demonstrate Mininet-WiFi’ s ability to be used as the Software Defined Network emulator which can also be integrated to the existing network using a Network Virtualized Function (NVF). A typical organization’s computer network was simulated which consisted of a website hosted on the LAMP (Linux, Apache, MySQL, PHP) virtual machine, and an F5 application delivery controller (ADC) which provided load balancing of requests sent to the web applications. A website page request was sent from the virtual stations inside Mininet-WiFi. The request was received by the application delivery controller, which then used round robin technique to send the request to one of the web servers on the LAMP virtual machine. The web server then returned the requested website to the requesting virtual stations using the simulated virtual network. The significance of these results is that it presents Mininet-WiFi as an emulator, which can be integrated into a real programmable networking environment offering a portable, cost effective and easily deployable testing network, which can be run on a single computer. These results are also beneficial to modern network deployments as the live network devices can also communicate with the testing environment for the data center, cloud and mobile provides. On the second test, a Software Defined Network was created in Mininet using python script. An external interface was added to enable communication with the network outside of Mininet. The amazon web services elastic computing cloud was used to host an OpenDaylight controller. This controller is used as a control plane device for the virtual switch within Mininet. In order to test the network, a webserver hosted on the Emulated Virtual Environment – Next Generation (EVENG) software is connected to Mininet. EVE-NG is the Emulated Virtual Environment for networking. It provides tools to be able to model virtual devices and interconnect them with other virtual or physical devices. The OpenDaylight controller was able to create the flows to facilitate communication between the hosts in Mininet and the webserver in the real-life network / The University of South Africa The University of Johannesburg / College of Engineering, Science and Technology