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.

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.

This thesis examines the benefits and drawbacks of cloud computing technology within the GLAM (Galleries, Libraries, Archives, and Museums) sector of Sweden and Finland. It employs the case study of the recently developed and launched Digital Archive Project at Åland Maritime Museum which leveraged the Amazon Web Services (AWS) technology stack to provide a cloud-based digital platform for the museum's archival materials. The primary objective of this study is to understand the interaction, usage, and suitability of cloud computing technologies and the impact of User Experience (UX) (primary users being the GLAM professionals) on digitalization efforts. This study analyzes eight GLAM institutions in Sweden and Finland using semi-structured interviews and compares the trust and readiness of adapting to private cloud service providers. The findings reveal that Finland has a more ‘aggressive’ and experimental approach to newer technologies such as cloud computing tools, compared to Sweden. In Sweden, there is an appreciation for pleasant UX and methods to make heritage material more accessible, but there is also a lot of hesitation due to the data privacy regulations in the aftermath of the Schrems II Judgment and the invalidation of the EU-U.S. Privacy Shield Agreement. The study concludes that AWS as a cloud provider is difficult to incorporate in the public sector GLAM institutions compared to the private sector. The study also provides practical recommendations for GLAM institutions and professionals and calls for further interdisciplinary research with Digital Humanists at the center of it.

<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>