Social media data can be collected and analyzed during and after crisis events to aid in disaster relief efforts. Setting up a system to perform this analysis is associated with various technical challenges. This thesis presents the design and implementation of a social media analysis as a service system. This system is hosted on cloud infrastructure and allows for on demand collection, processing, and delivery of social media data via an intuitive web interface. The service model and cloud infrastructure allow the system to be highly scalable and produce near real time analysis data. A cost evaluation of the system was performed and presented in this thesis. The SMAS system was evaluated through a user study and participants found the system unique, interesting, and easy to understand and use. The usability of the system was also evaluated during the user study and resulted in a system usability score of 79.25 out of 100. Suggestions to improve the system and increase its adoptability are also discussed. The thesis concludes with suggestions for future work.

<p>U ovoj doktorskoj disertaciji ispitana je mogućnost prebacivanja Naprednog Distributivnog Menadžment Sistema (NDMS) zasnovanog na zajedničkoj operaciono tehnološkoj platformi na virtualno Cloud infrastrukturno okruženje. Prvo je odabrano tradicionalno NDMS rešenje zasnovano na fizičkoj računarskoj arhitekturi i identifikovani su funkcionalni blokovi. Zatim su profilirane njihove performanse prema četiri ključna resursa: procesor, operativna memorija, računarska mreža i stalna memorija. Dalje predloženo je virtualno rešenje zasnovano na Cloud infrastrukturi koje je verifikovano na dve zamišljene elektrodistributivne mreže realnih veličina (maloj i velikoj) i u dva testna scenarija (stabilnog stanja i visoke aktivnosti). Na kraju predstavljeni su rezultati testiranja koji pokazaju da se NDMS može prebaciti u virtualno Cloud okruženje bez negativnog uticaja na funkcionalne i nefunkcionalne zahteve NDMS rešenja.</p> / <p>This dissertation inspects the possibility of transferring the Advanced Distribution Management System (ADMS) based on a common operating technology platform to the virtual Cloud Infrastructure Environment. First, a traditional ADMS solution based on physical computing architecture was chosen and functional blocks were identified. Furthermore, their performances were profiled to four key resources: processor, operating memory, computer network, and storage. Then, virtual cloud-based solution was proposed based on Cloud infrastructure which is verified on two imaginary power distribution networks of real size (small and large) and in two test scenarios (steady state and high activity). Finally, the test results show that ADMS can be transferred to a virtual Cloud environment without adversely affecting the functional and non-functional requirements of the ADMS solution.</p>

This thesis was prepared in collaboration with Acreo (Research and Development Company) and OpenCare (IT Company). Its purpose was to design and develop a research testbed to enable context awareness in pervasive environments by modifying the MediaSense framework (EU Funded project) of Mid Sweden University. In ubiquitous environments, the proliferation of devices such as sensors, active badges, mobile phones, RFID and NfC tags enables the development of intelligent services towards new forms of pervasive applications. These intelligent services seamlessly gather context information and the benefits offered are in the provision of better services. The inspiration given by these intelligent services has meant that the focus of thesis has been on using these services in a healthcare application. The challenge is that the proposed testbed should address the intelligent delivery of health information to any host, anywhere, based on the user’s context. Further, context reasoning requires substantial computing power and smart devices have limited resources in terms of processing and memory, so, the testbed should enable smart communication to take place between these devices. The proposed solution satisfies the stated requirements by using a cloud infrastructure and a Distributed Context eXchange Protocol (DCXP). Any device on the internet that is DCXP capable may register with the architecture and share context information in an efficient way. In order to view context information, TV, smartphones, internet tablets and web interfaces have been provided for both the user and the health centre. By successfully addressing the requirements of the testbed, this thesis has enabled the creation of a pervasive healthcare application. Hence, this thesis concludes with the observation that the proposed approach for context awareness in a healthcare system has the ability to deal with the stated challenges.

This thesis aims to assess the security of Oracle Cloud Infrastructure (OCI) through penetration testing of some of its services. Targeted at cloud, cybersecurity, governance, and compliance professionals as well as administrators or cyber enthusiasts in general, this research uncovers specific best practices to OCI. We employ a methodology in three steps published by Astra aimed at cloud services auditing, combining penetration testing techniques and thorough documentation review to evaluate the security posture of OCI services. The scope encompasses IAM and MySQL Managed Databases. We found that unproperly supervised ABAC policies could lead to privilege escalation through the tagging of computing resources and that the MySQL service does not present the major issues that occurred in the managed services of OCI’s main competitors. This research contributes to the growing body of knowledge on cloud security and offers practical recommendations to strengthen OCI deployments, ultimately fostering greater confidence in adopting OCI services. / Syftet med denna uppsats är att undersöka säkerheten hos Oracle Cloud Infrastructure (OCI) genom penetrationstestning av några av dess tjänster. Riktad till moln-, cybersäkerhets-, styrnings- och efterlevnadsproffs, bidrar denna forskning till best-practice metoder för OCI. Vi tillämpar en metodik i tre steg som publicerats av Astra och som är inriktad på granskning av molntjänster. Metodiken kombinerar tekniker för penetrationstester och noggrann dokumentationsgenomgång för att utvärdera säkerhetsläget för OCI. Omfattningen inkluderar IAM och hanterade MySQL-databaser. Vi fann att bristfälligt övervakade ABAC-policyer kunde leda till privilegieeskaleringsproblem genom taggning av beräkningsresurser och att Oracles MySQL-tjänst inte har de större problem som hittades i hanterade tjänster hos OCIs främsta konkurrenter. Denna forskning bidrar till den växande kunskapsmängden om molnsäkerhet och erbjuder praktiska rekommendationer för att stärka implementeringar av OCI, vilket i slutändan främjar större förtroende för och antagandet av OCItjänster.

Technology development in the sub-field of older adult care has always been on the back-burner compared to other healthcare areas. But with increasing life expectancy, this is poised to change. With the increasing older adult population, the current older adult care facilities and personnel are struggling to keep up with demand. Research conducted in the Netherlands [1] found 33,000 older adults were awaiting admission into a home for the elderly showing that demand far exceeds availability. This huge demand for older adult care has resulted in a decrease in the quality of care being provided. A recent study involving older adults aged 65 and above [2] compared the quality of care given to older adults in nursing homes in the UK and found it to be inadequate. While it is true that giant strides have been made in the field of personal health and fitness [3], we have to acknowledge that these technologies have not found widespread adoption in the elderly communities for a number of reasons which include lack of education, cognitive impediments, low-income and techno-phobia [4]. We believe that older adult care technologies should be approached from a different perspective in order to maximize outcomes. Inventions in the health care space are a moving target and a significant degree of technical aptitude and interest is required to keep up with these changes. My research work will be focused on developing a distributed system infrastructure that will enable large-scale monitoring of vital signals and early detection of emergency situations in nursing homes and assisted living communities. This new approach will increase automation in nursing homes leading to a reduction in running cost and an increase in capacity

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.

This thesis explores the sustainability challenges and opportunities within the data center sector, with a specific focus on Atea, a leading player in the digital infrastructure industry. As organizations increasingly depend on IT systems for operational efficiency, the environmental impact of data centers has come under scrutiny. With the increasing demands for sustainability driven by both market forces and stringent EU regulations, this study examines how organizations, particularly within the data center industry, can adapt to meet these emerging requirements. This study aims to contribute an understanding of strategies and measures that can be implemented to achieve more sustainable data center operations. Our research employed a qualitative methodology, incorporating both literature review and a case study to gather insights into sustainable practices in the data center industry. Atea’s role as a case study provided practical perspective on the implementation of sustainability strategies within the sector. The findings highlight significant challenges, including energy consumption, waste management, and regulatory compliance. Conversely, the study also identifies opportunities such as the adoption of green technologies, improved energy efficiency, and strategic waste reduction practices. The implications of this research are twofold: it offers a roadmap for data centers aiming to enhance their sustainability profile, and it contributes to the broader discourse on the environmental responsibilities of the IT-sector. Future research should investigate the real-world impact of new EU regulations in the data center sector, focusing on how national legislation aligns with EU requirements and their practical application by data centers.

Cloud Infrastructure-as-a-Service (IaaS) is a form of utility computing which has emerged with the recent innovations in the service computing and data communication technologies. Regardless of the fact that IaaS is attractive for application service providers, satisfying user requests while ensuring cloud operational objectives is a complicated task that raises several resource management challenges. Among these challenges, limited controllability over network services delivered to cloud consumers is prominent in single datacenter cloud environments. In addition, the lack of seamless service migration and optimization, poor infrastructure utilization, and unavailability of efficient fault tolerant techniques are noteworthy challenges in geographically distributed datacenter clouds. Initially in this thesis, a datacenter resource management framework is presented to address the challenge of limited controllability over cloud network traffic. The proposed framework integrates network virtualization functionalities offered by software defined networking (SDN) into cloud ecosystem. To provide rich traffic control features to IaaS consumers, control plane virtualization capabilities offered by SDN have been employed. Secondly, a quality of service (QoS) aware seamless service migration and optimization framework has been proposed in the context of geo-distributed datacenters. Focus has been given to a mobile end-user scenario where frequent cloud service migrations are required to mitigate QoS violations. Finally, an SDN-based dynamic fault restoration scheme and a shared backup-based fault protection scheme have been proposed. The fault restoration has been achieved by introducing QoS-aware reactive and shared risk link group-aware proactive path computation algorithms. Shared backup protection has been achieved by optimizing virtual and backup link embedding through a novel integer linear programming approach. The proposed solutions significantly improve bandwidth utilization in inter-datacenter networks while recovering from substrate link failures.

Migration to the cloud has been a popular topic in industry and academia in recent years. Despite many benefits that the cloud presents, such as high availability and scalability, most of the on-premise application architectures are not ready to fully exploit the benefits of this environment, and adapting them to this environment is a non-trivial task.Therefore, many organizations consider a gradual process of moving to the cloud with Hybrid Cloud architecture. In this paper, the author is making an effort of analyzing particular enterprise case in cloud migration topics like cloud deployment, cloud architecture and cloud management.This paper aims to identify, classify, and compare existing challenges in cloud migration, illustrate approaches to resolve these challenges and discover the best practices in cloud adoption and process of conversion teams to the cloud.

The machine learning (ML) industry has taken great strides forward and is today facing new challenges. Many more models are developed, used and served within the industry. Datasets that models are trained on, are constantly changing. This demands that modern machine learning processes can handle large number of models, extreme load and support recurring updates in a scalable manner. To handle these challenges, there is a concept called model serving. Model serving is a relatively new concept where more efforts are required to address both conceptual and technical challenges. Existing ML model serving solutions aim to be scalable for the purpose of serving one model at a time. The industry itself requires that the whole ML process, the number of served models and that recurring updates are scalable. That is why this thesis presents an open-source framework for large-scale ML model serving that aims to meet the requirements of today’s ML industry. The presented framework is proven to handle a large-scale ML model serving environment in a scalable way but with some limitations. Results show that the number of parallel requests the framework can handle can be optimized. This would make the solution more efficient in the sense of resource utilization. One avenue for future improvements could be to integrate the developed framework as an application into the open-source machine learning platform STACKn.