Abstract This paper is a thesis work during the Computer Science Bachelor programme at Malmö University spring 2022. The thesis conducts a litterature review on benefits and risks of legacy applications to cloud. What are the challenges of migrating legacy applications to cloud? Why do businesses migrate legacy applications to cloud in relation to the challenges presented in the litterature? The thesis reviews ten research articles and a number of books in order to answer and discuss the questions. It concludes that in spite of a great number of challenges presented in the litterature, both technical and operational, businesses still see the benefits of legacy application migration and perform the back breaking technical migrations of monolithic architecures in legacy applications, to modern, fast-deploying microservices. This in order to achieve cost reductions, fast deployments and reduced maintenance in software over time. / Uppsatsen är ett kandidatarbete inom ramen för Systemutvecklarprogrammet 180 hp på Malmö Universitet och riktar sig till systemutvecklarstudenter. Den syftar till att öka kunskapen om migration av legacyapplikationer till molnet och diskuterar detta utifrån frågeställningarna ”Vilka utmaningar finns det med migrationer till molnet?” och ”Varför väljer företag att migrera legacy applikationer till cloudmiljö i relation till de utmaningar som beskrivs i litteraturen?”. Uppsatsen bygger på en litteraturstudie där flera av artiklarna är systematiska litteraturstudier där omfattande arbete har analyserats av andra forskare inom sektorn och visar att migration av legacyapplikationer åtföljs av en stor rad utmaningar så som byte av arkitektur – från traditionella monolitiska till serviceorienterade så som microservices, omskrivning av programkod och kulturella förändringar inom verksamheten. Det finns också olika drivkrafter bakom en migration, så som snabbare distribuering av tjänster genom microservices, besparingar gällande kostnader och personal eller skalbarhet. Dessa drivkrafter visar sig vara värda mödan för en del företag, eftersom en ständig ökning av molnkunder registreras fortfarande och för att kunna konkurrera mot andra, likartade företag blir migrationen ett krav.

To achieve the best performance out of an IaaS cloud, the resource management layer must be able to distribute the workloads it is tasked with optimally on the underlying infrastructure. A utilization-based scheduler can take advantage of the fact that allocated resources and actual resource usage often differ to make better-informed decisions of where to place future requests. This thesis presents the design, implementation and evaluation of an initial placement controller that uses host utilization data as one of its inputs to help place virtual machines according to one of a number of supported management objectives. The implementation, which builds on top of the OpenStack cloud platform, deals with two different objectives, namely, balanced load and energy efficiency. The thesis also discusses additional objectives and how they can be supported. A testbed and demonstration platform consisting of the aforementioned controller, a synthetic load generator and a monitoring system are built and used during evaluation of the system. Results indicate that the scheduler performs equally well for both objectives using synthetically generated request patterns of both interactive and batch type workloads. A discussion of current limitations of the scheduler and ways to overcome those conclude the thesis. Among the things discussed are how the rate at which host utilization data is collected limits the performance of the scheduler and under which circumstances dynamic placement of virtual machines must be used to complement utilization-based scheduling to avoid the risk of overloading the cloud. / För att erhålla maximal prestanda ur ett IaaS moln måste dess resurshanteringssystem kunna schemalägga resursutnyttjandet av den underliggande infrastrukturen på ett optimalt sätt. En nyttjandebaserad schemaläggare kan dra nytta av det faktum att allokerade resurser och faktiskt använda resurser ofta skiljer sig åt, för att på så sätt kunna fatta mer välgrundade beslut om var framtida förfrågningar skall placeras. Detta examensarbete presenterar såväl utformning, implementation samt utvärdering av en kontrollenhet för initial placering som till sin hjälp använder information om värdutnyttjande som indata för placering av virtuella maskiner enligt ett av ett antal stödda förvaltningssyften. Implementationen, som baseras på molnplattformen OpenStack, behandlar två sådana syften, balanserad last och energieffektivitet. I tillägg diskuteras krav för att stödja ytterligare syften. En testmiljö och demonstrationsplattform, bestående av ovan nämnda kontrollenhet, en syntetisk lastgenererare samt en övervakningsplattform byggs upp och används vid utvärdering av systemet. Resultat av utvärderingen påvisar att schemaläggaren presterar likvärdigt för båda syftena vid användande av last bestående av såväl interaktiva som applikationer av batch-typ. En diskussion om nuvarande begränsningar hos schemaläggaren och hur dessa kan överbryggas sammanfattar arbetet. Bland det som tas upp diskuteras bl.a. hur hastigheten vid vilken värdutnyttjande data samlas in påverkar prestandan hos schemaläggaren samt under vilka förhållanden dynamisk placering av virtuella maskiner bör användas som komplement till nyttjandebaserad schemaläggning för att undvika risk för överbelastning i molnet.

This thesis examines the life cycles of a data centre and a local server to conclude which of them has the largest environmental impact. Data centres are warehouse-looking buildings with servers in them, hosting the so-called “cloud”. Storing your data and information in a data centre and using its software and other resources through the internet is called cloud computing. The more traditional alternative to cloud computing is to have your own servers locally. You can access these without the internet, but you must operate and service it by yourself. There are some benefits with using cloud computing instead of local servers. One of these is often said to be energy efficiency and by that less environmental impact. This thesis sets out to confirm or refute, that statement. To do that, a comparing Life Cycle Assessment (LCA) has been done. A comparing LCA is a method to compare two similar systems and how much environmental impact they both have had during their lifetime. To assess this the amount of carbon dioxide equivalents emissions per the number of virtual machines has been used as a functional unit. The assessment was made on a hypothetical data centre and a hypothetical local server. The results showed that the local server had around 9.5 more carbon dioxide emissions per virtual machine than the data centre which confirms that the data centre has the lesser environmental impact of the two. This was mostly because of a more efficient use of the servers and the fact that a data centre has the possibility to recover some of its waste heat.

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Stress is now recognized as one of the major causes of physical and psychological illness. It is known as a reaction to surrounding environmental threats and the best way to manage it is to understand its triggers. Although people continuously react to their surrounding environments, they sometimes are not aware that certain elements in their environment are considered to be stressors. Based on this fact, researchers have recently proposed context-aware stress management systems. Most of the proposed systems use context data to provide real time stress monitoring and visualization, along with intervention techniques. However, these interventions are limited to the second and tertiary stages and very little attention has been given to the primary stage. In this thesis, we introduce a system called CASP. The system’s objective is to provide stress status predictions based on a user’s current contextual data. Therefore, a detection method is developed using heart rate variability (HRV) as a stress indicator to deliver personalized context-aware stress reports. Based on the predicted status, the system provides users with stress interventions at an early stage in order to help avoid and/or eliminate the occurrence of stress. Our evaluation results show that the CASP system is able to predict the stress status of a user with an averaged accuracy of 78.23% through our limited activity, when compare to a stress status measured using physiological signals. Moreover, it provides prediction models that adapt to the changing nature of both the user’s stress status and the surrounding environment.

User generated reviews, like those found on Yelp and Amazon, have become important reference material in casual decision making, like dining, shopping and entertainment. However, very large amounts of reviews make the review reading process time consuming. A text visualization can speed up the review reading process. In this thesis, we present the clustered layout word cloud -- a text visualization that quickens decision making based on user generated reviews. We used a natural language processing approach, called grammatical dependency parsing, to analyze user generated review content and create a semantic graph. A force-directed graph layout was applied to the graph to create the clustered layout word cloud. We conducted a two-task user study to compare the clustered layout word cloud to two alternative review reading techniques: random layout word cloud and normal block-text reviews. The results showed that the clustered layout word cloud offers faster task completion time and better user satisfaction than the other two alternative review reading techniques. [Permission email from J. Huang removed at his request. GMc March 11, 2014] / Master of Science

Thesis. 1977. Sc.D.--Massachusetts Institute of Technology. Dept. of Meteorology. / Microfiche copy available in Archives and Science. / Vita. / Bibliography : leaves 249-256. / Sc.D.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / In recent years, the increased use of digital tools within the Peruvian tourism industry has created a corresponding increase in revenues. However, both factors have caused increased competition in the sector that in turn puts pressure on small and medium enterprises’ (SME) revenues and profitability. This study aims to apply neural network based sentiment analysis on social networks to generate a new information search channel that provides a global understanding of user trends and preferences in the tourism sector. A working data-analysis framework will be developed and integrated with tools from the cloud to allow a visual assessment of high probability outcomes based on historical data, to help SMEs estimate the number of tourists arriving and places they want to visit, so that they can generate desirable travel packages in advance, reduce logistics costs, increase sales, and ultimately improve both quality and precision of customer service.

When considering migrating a system to the cloud, one important aspect of the process is to identify which components of the system are suitable and beneficial to deploy in cloud. This process is crucial and can be the difference between a successful deployment and a failure. The Media Asset Management system (MAM) Cantemo, is in this thesis examined with the purpose of identifying suitable components for deployment based on suitability and practicability criteria lists. In addition, some of the identified components are chosen to be migrated to Amazon Web Services (AWS). Lastly, these deployments are evaluated by putting them through multiple, self-assessed, tests to check if the system can handle basic tasks such as ingestion of files and transcoding of images. The results show only one component was migrated and successfully passed the self-assessed tests, due to time constraints.

Since the introduction of cloud computing as a standard solution for data storage and software hosting, new security measures have been developed, and data laws and compliance regulations have become more stringent. In this thesis, the exploration of compliance and regulatory documents, interviews of industry professionals, and thematic analysis of interview data  uncover some industry-preferred implementation practices that will help to ensure compliance and cloud security for applications and data storage on the cloud. Industry professionals' opinions are put into context and compared to compliance regulations. Key findings include a list of implementation practices through thematic analysis of interview data put into themes. These include encryption, security controls, life-cycle management, and audits. The findings' importance is a list of practices or actions that any developer can proactively adapt for the cloud and know that it is a viable implementation to remain secure. The limitations and scope is related to cloud systems and software engineering, and the list extracted from the interview data is not an all-encompassing solution for cloud security.  Furthermore, the raw interview data will assist future research into this topic.