With the popularity of the Internet and the wider bandwidth, more and more people watch streaming movies online. The larger the scale of the web site, the more load it has to handle. Thus, how to efficiently process users' queries, reduce network latency and packet loss, and improve data reliability at once are top issues. Cloud environments, in this thesis, are used to solve these problems. Also, a cloud-based streaming system that enables users query movie information and watch movies streaming online is designed and implemented to deliver compelling user experiences.

Organisations are facing increased competition in contemporary business environments. At the same time, cloud computing is a catalyst for new software applications and services available to organisations. Therefore, cloud computing is a viable option to provide innovation within the organisation. Therefore, organisations need to recognise the potential transformation of its business model, to take advantage of cloud computing. This research sets out to describe and explain the relationship between the various business strategy factors and CC. Organisations have to guard against using cloud-computing capabilities only to provide organisational efficiencies, as the efficiencies gained do not always translate into business value. Adopting cloud computing can cause disruptions in the organisation. Therefore, the organisation needs a strategy and understand the relationship between the business strategy and the cloud computing options available. The present study performs multi-method qualitative research, within the South African context. By taking a constructivist view, the researcher believes the knowledge will emerge from the interaction the people have with their environment. The research purpose states the research as descriptive and explanatory. Data collection for the present study performs face-to-face interviews. A general interview-guided approach ensures the research covers same areas of interest in all the interviews. For the data analysis, the researcher uses an inductive thematic analysis method. Software-as-a-Service influences the customer behaviour and forces organisations to re-evaluate their use of cloud computing. However, new cloud computing capabilities brought into the organisation need to provide a value proposition with an expected time-to-market. Also, large organisations require a technology architecture review to assess the impact on their infrastructure. The multi-faceted cost structure coupled with legacy systems and legacy investment products can prevent the adoption of cloud computing. Another factor is the vendor relationship and their influence regarding the solutions into which an organisation invests. The present study concludes how cloud computing offers no competitive differentiation for South African investment services organisations. For these organisations, their existing business models remains profitable. Business strategy, therefore, has no compelling reason to consider cloud computing. Furthermore, information technology is a utility service. For these organisations, the information technology and business strategy align through the service-level method. This alignment method forces the information technology department to focus on maintaining a stable and reliable infrastructure. Cloud computing is only considered when contributing to the service-level. A misalignment then follows, and individual business units adopt cloud computing to fulfil their business need. As a result, the business unit is ready to adopt cloud computing while the information technology department is a hindrance towards adopting cloud computing. Software-as-a-Service solutions are the most used cloud computing option, based on its ability to offer an accelerated time-to-market for proof-of-concept products and services. However, most final business solutions move onto the internal infrastructure of the organisation. Platform-as-a-Service and Infrastructure-as-a-Service are used to a lesser degree by organisations in this study.

Det blir allt vanligare att spara data online, i stället för på fysiska lagringsmedium. Detta bringar många möjligheter för dig som användare, men orsakar också nya problem framför allt inom utredningsarbetet. Problemen i kombinationen IT-forensik och molntjänster kan framför allt delas upp i två kategorier, vilka är juridiska respektive tekniska problem. De juridiska problemen berör främst att servern som lagrar data och ägaren till denna ofta befinner sig i en annan nation än där det misstänkta brottet utreds. De flesta juridiska problem kan tyckas enkla att lösa genom lagändringar, men är mer omfattande än så då både de konsekvenser det kan ha för molnleverantörerna, liksom de fördelar det kan ha för rättsväsendet, måste tas hänsyn till och noga övervägas. De tekniska problemen finns det ofta redan lösningar på. Många av dessa kan dock inte anses vara reella då krävd storlek på lagringsytan, och kostnaderna därtill, inte är i proportion av vad som skulle kunna uppnås. De flesta tekniska lösningar ger även nya problem i form av etiska dilemman då de kräver utökad lagring av personlig information. Att spara information och eventuellt behöva utreda information kopplat till en person som inte är misstänkt gör intrång på den personliga integriteten. Molnet har dock också möjligheter där den främsta för IT-forensiken är vad som kallas Digital Forensics as a Service. Detta innebär att molnets resurser nyttjas för att lösa resurstunga problem som hade varit betydligt mer tidskrävande att genomföra lokalt, likaså att möjligheterna för samarbeten och specialkompetens ökar, i syfte att underlätta och effektivera det IT-forensiska arbetet. / It is becoming more common to save data online, rather than on physical storage media. This brings many opportunities for you as a user, but also causes new problems, especially within the crime investigations. The problems in the combination of digital forensics and cloud services can be divided into two main categories, which are legal issues and technical issues. The legal issues primarily concern that the server that stores data and the owner of the server is typically based in a different nation than where the suspected crime is investigated. Most legal issues may seem easy to solve through law changes, but are more extensive than that, as both the consequences it may have for the cloud suppliers, as well as the benefits it may have for the justice system, must be taken into consideration. The technical issues often have solutions. However, many of these cannot be considered as realistic since the size of the required storage space, and the costs caused by it, are not proportional to what could be achieved. Most technical solutions also give rise to new issues in the form of ethical dilemmas as they require enhanced storage of personal information. To save more information and to possibly need to investigate information associated with a person who is not suspected of committing the crime intrudes the personal integrity. The cloud, however, also brings opportunities where the foremost for digital forensics is what is called Digital Forensics as a Service. This means that the cloud’s resources are utilised to solve resource related problems that had been significantly more time consuming to implement locally, as well as the opportunities for cooperation and expertise increase, in order to facilitate and enhance IT-forensic work.

Background: Cloud computing’s rapid growth has highlighted the need for efficientresource allocation. While cloud platforms offer scalability and cost-effectiveness for a variety of applications, managing resources to match dynamic workloads remains a challenge. Auto-scaling, the dynamic allocation of resources in response to real-time demand and performance metrics, has emerged as a solution. Traditional rule-based methods struggle with the increasing complexity of cloud applications. Machine Learning models offer promising accuracy by learning from performance metrics and adapting resource allocations accordingly.  Objectives: This thesis addresses the topic of cloud environments auto-scaling recommendations emphasizing the integration of Machine Learning models and significant application metrics. Its primary objectives are determining the critical metrics for accurate recommendations and evaluating the best recommendation techniques for auto-scaling. Methods: The study initially identifies the crucial metrics—like CPU usage and memory consumption that have a substantial impact on auto-scaling selections through thorough experimentation and analysis. Machine Learning(ML) techniques are selected based on literature review, and then further evaluated through thorough experimentation and analysis. These findings establish a foundation for the subsequent evaluation of ML techniques for auto-scaling recommendations. Results: The performance of Random Forests (RF), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM) are investigated in this research. The results show that RF have higher accuracy, precision, and recall which is consistent with the significance of the metrics which are identified earlier. Conclusions: This thesis enhances the understanding of auto-scaling recommendations by combining the findings from metric importance and recommendation technique performance. The findings show the complex interactions between metrics and recommendation methods, establishing the way for the development of adaptive auto-scaling systems that improve resource efficiency and application functionality.

Apache Spark is a popular open-source distributed processing framework that enables efficient processing of large amounts of data. Apache Spark has a large number of configuration parameters that are strongly related to performance. Selecting an optimal configuration for Apache Spark application deployed in a cloud environment is a complex task. Making a poor choice may not only result in poor performance but also increases costs. Manually adjusting the Apache Spark configuration parameters can take a lot of time and may not lead to the best outcomes, particularly in a cloud environment where computing resources are allocated dynamically, and workloads can fluctuate significantly. The focus of this thesis project is the development of an auto-tuning approach for Apache Spark configuration parameters. Four machine learning models are formulated and evaluated to predict Apache Spark’s performance. Additionally, two models for Apache Spark configuration parameter search are created and evaluated to identify the most suitable parameters, resulting in the shortest execution time. The obtained results demonstrates that with the developed auto-tuning approach and adjusting Apache Spark configuration parameters, Apache Spark applications can achieve a shorter execution time than when using the default parameters. The developed auto-tuning approach gives an improved cluster utilization and shorter job execution time, with an average performance improvement of 49.98%, 53.84%, and 64.16% for the three different types of Apache Spark applications benchmarked. / Apache Spark är en populär öppen källkodslösning för distribuerad databehandling som möjliggör effektiv bearbetning av stora mängder data. Apache Spark har ett stort antal konfigurationsparametrar som starkt påverkar prestandan. Att välja en optimal konfiguration för en Apache Spark-applikation som distribueras i en molnmiljö är en komplex uppgift. Ett dåligt val kan inte bara leda till dålig prestanda utan också ökade kostnader. Manuell anpassning av Apache Spark-konfigurationsparametrar kan ta mycket tid och leda till suboptimala resultat, särskilt i en molnmiljö där beräkningsresurser tilldelas dynamiskt och arbetsbelastningen kan variera avsevärt. Fokus för detta examensprojekt är att utveckla en automatisk optimeringsmetod för konfigurationsparametrarna i Apache Spark. Fyra maskininlärningsmodeller formuleras och utvärderas för att förutsäga Apache Sparks prestanda. Dessutom skapas och utvärderas två modeller för att söka efter de mest lämpliga konfigurationsparametrarna för Apache Spark, vilket resulterar i kortast möjliga exekveringstid. De erhållna resultaten visar att den utvecklade automatiska optimeringsmetoden, med anpassning av Apache Sparks konfigurationsparameterar, bidrar till att Apache Spark-applikationer kan uppnå kortare exekveringstider än vid användning av standard-parametrar. Den utvecklade metoden för automatisk optimering bidrar till en förbättrad användning av klustret och kortare exekveringstider, med en genomsnittlig prestandaförbättring på 49,98%, 53,84% och 64,16% för de tre olika typerna av Apache Spark-applikationer som testades.