Context: Cloud Computing has advanced in recent years. It is catching people’s attention as a commodious resource of computational power. Slowly, Cloud is bringing new possibilities for a scientific community to build High Performance Computing platforms. Despite the wide benefits the Cloud offers, the question on everyone’s mind is “Whether the Cloud is a feasible platform for HPC applications”. This thesis evaluates the performance of the Amazon Cloud using a sorting benchmark. Objectives: 1. To investigate all the previous work on HPC that has been ported to the Cloud environment in various fields. Also, the problems and challenges are assessed relevant to HPC associated with the Cloud. 2. A study is done on how to implement parallel Quicksort efficiently to obtain good Speedup. 3. A parallel Quicksort is developed and its performance is measured using ‘Speedup’ by deploying in the Cloud. Methods: Two different research methods were used to carry out the research. They are Systematic Literature Review (SLR) and a Quantitative methodology. Research papers from academic databases namely IEEE Xplore, Inspec, ACM Digital Library and Springerlink were chosen for conducting SLR. Results: From the systematic review undertaken, 12 HPC applications, 9 problems and 5 challenges in the Cloud were identified. Efficient way to implement the parallel Quicksort on the Cloud has been identified. From the experiment results, a low Speedup is obtained in a Cloud environment. Conclusions: Many HPC applications which were deployed in the Cloud so far were identified along with problems and challenges. Message Passing interface (MPI) is chosen as the efficient method to develop and implement the parallel Quicksort in the Cloud. From the experiment results, we believe that the Cloud is not a suitable platform for HPC applications.

Context: Cloud computing is reshaping the service-delivery and business-models in Information and Communications Technology (ICT). The Information Technology (IT) sector has benefited from it in the previous 3-5 years. Despite the attraction of cloud computing, it is required to have an effective application migration strategy. Cloud computing with its diverse provisioning models makes it possible for telecom vendors and service providers to decide effective service and business models. Currently, cloud computing contains security, performance and dimensioning considerations for telecom companies. Objectives: This thesis assesses the trends and issues associated with the cloud, with telecommunications perspective, while leveraging the cloud to come to a decision on a suitable cloud environment for telecom grade applications. Analysis of maturity of public cloud (in terms of compatibility, consolidation, compliance and standardization) in general and Amazon cloud in particular, is part of the thesis objective. While doing so, deployment of a telecom-grade product in the Amazon cloud will be evaluated against the current on-premise deployment. We want to identify architectural difference between the two domains, and what issues are faced when a migration is planned. This evaluation between two systems, i.e. on-premise and the cloud will significantly contribute to the research and can be used when making business decisions. Methods: We conducted literature review, survey, and a case study, assessing the above mentioned objectives. Research papers from academia and industry were chosen for literature review; personnel, with experience in cloud computing, were chosen for the survey; and a telecom-grade platform was used to assess the migration issues on Amazon cloud in the case study. The Ericsson Composition Engine (ECE) was used to check what deployment issues it can have on Amazon cloud. Its on-premise Reference Deployment Architecture was compared with the cloud-based Reference Deployment Architecture. This case study served as a confirmation to results obtained in the literature review and survey. Results: In the literature review and survey, we found motivations, trends, current applications, and challenges of cloud computing for telecom. It was found from the case study that Amazon Web Services (AWS) lacks application and network centric attributes that are required in ECE deployment. We propose recommendations that can be integrated with ECE while deploying it in a public cloud. Conclusions: Companies are choosing cloud vendors that uniquely give ease of migration and control, based on application needs and compatibility. ECE cannot be directly migrated to AWS, unless we provide Amazon specific modifications in the architecture. The survey and literature review support a private and/or hybrid strategy for ECE, along with the inclusion of cloud networking into the ECE package.

Molntjänster av olika slag har blivit allt vanligare i samhället de senaste åren. Bland de mest använda molntjänsterna finns Dropbox, Microsoft Azure och Amazon Web Services (AWS). Den sistnämnda molntjänsten är något som företaget Infor har fått ögonen på. AWS erbjuder inte endast datalagring som Dropbox gör utan även dataexekvering, databaslösningar, nätverkslösningar, driftsättning och hantering av datorsystem. Den här rapporten kommer gå igenom hur AWS molntjänster har använts för att testa en av Infors produkter på ett distribuerat system i molnet istället för på egen hårdvara. De molnlösningar som kommer att undersökas i denna rapport är främst AWS OpsWorks och AWS CloudFormation tillsammans med programmet Chef. / Cloud computing services of various kinds have become increasingly common in society in recent years. Among the most widely used cloud computing services are Dropbox, Microsoft Azure and Amazon Web Services (AWS). The latter service is something that the company Infor have got their eyes on. AWS offers not only data storage like Dropbox does, but also data execution, database solutions, network solutions, deployment and management of computer systems. This report will go over how AWS have been used to test one of Infor's products on a distributed system in the cloud instead of on their own hardware. The cloud computing services that will be examined in this report are primarily AWS OpsWorks and AWS CloudFormation, together with the program Chef.

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The first chapter of this thesis is motivated by a puzzle in consumer finance. In high-stakes financial decisions, people leave a substantial amount of money on the table, even when financial education is available. The ubiquity of financial choices makes understanding the effects of incentives and education on mistakes crucial. This chapter experimentally examines the impact of changes in incentives and educational availability on incentivized but hypothetical healthcare choices using Amazon Mechanical Turk. We find that increasing incentives are ineffective in increasing decision-making effort, even when these changes are made clear and salient to the subjects. Yet, surprisingly, despite this lack of effort response, subjects' choices improve when incentives are high. This result highlights an under-appreciated channel of incentives: when stakes become larger, often, the problems become simpler too. We next investigate the effect of available education. Overall, education leads to an increase in decision-making effort and an improvement in choice quality. However, this average effect masks significant heterogeneity across incentive treatments. Subjects are willing to put in the educational effort when either the problems are hard or mistakes are highly costly, but the return of the educational effort is zero for hard problems and positive for easy ones. Thus, only when stakes are high and the problem is easy does education have an effect. These findings suggest that people can be encouraged to get education for high-stakes decisions, and policy-makers have a role in simplifying problems to translate the extra effort into better choices. The second chapter dives deeper into the "easiness" channel of incentives. This chapter uses an experiment to disentangle "easiness" from the standard incentives on savings account choices, again using Amazon Mechanical Turk. We show that increasing the variance of the accounts improves choices without increasing time spent. This is true in both between-subject and within-subject analyses. Besides, we re-investigate the effects of incentives and education. We recover the usual effects of incentives, where paying subjects a higher rate motivates them to spend more time and do better. We also find that easiness and incentives complement education. Consistent with the literature, we show that the effectiveness of education diminishes with time, suggesting that education should be provided as and when people make decisions. In the third chapter finds experimental evidence for preference for flexibility (PFF). Although PFF is very intuitive, documenting PFF experimentally faces challenges from stochastic choices. Because there are random noises in decision-making, experimental data may over-estimate PFF due to such randomness. This chapter tackles stochastic choices by first deriving theoretical upper bounds for PFF. We then measure PFF against these upper bounds using menu choices presented in the Multiple Price Lists (MPLs) in a lab experiment. We find that subjects exhibit more PFF than what can be explained by random noises. Specifically, there are more PFF than two countervailing behaviors, indifference and preference for commitment. We then present two alternative models for PFF, which have different predictions for the effect of the probability of payment on PFF. We suggest a modified experiment to tell these two models apart for future research.

This thesis aims to design and develop a platform based on a novel concept - the Internet of Robotic Things (IoRT) constructed by a robotic platform, an Internet of Things (IoT) platform and cloud computing services. A robotic platform enables hardware abstraction, facilitating the management of input/output between software, mechanical devices  andelectronic systems. The IoT platform is a global network enabling a massive number of devices known as things to communicate with each other and transfer data over the Internet. Cloud computing is a shared pool of scalable hardware usually provisioned as cloud services by third party cloud vendors. The integration of these concepts constitutes the core of the IoRT platform, as a global infrastructure facilitating robots to interconnect over the Internet utilizing common communication technology. Moreover, the pool of cloud resources shared by the connected robots enables scalable storage and processing power. The IoRT platform developed in this study constitutes firstly of the Amazon Web Service (AWS) IoT core serving as the IoT platform. Secondly, it incorporates the Robot Operating system (ROS) as the robotic platform and thirdly the cloud services Amazon DynamoDB and AWS Lambda for data storing and data processing respectively.The platform was evaluated in terms of delays & utilization and visualization capabilities. The platform demonstrates promising result in terms of delays exchanging small packages of data, round-trip delays in order of 50-60ms were obtained between a robot placed in Stockholm and the communication platform AWS IoT placed in Dublin, Ireland. Most of the delay is due to the traveling distance, where a round trip ping between Stockholm and Dublin takes around 50ms. The platforms ability to visualize streaming data from the robots, enables an operator to visualize selected data from any service in the platform over the Internet in near real-time, with round-trip delays in order of 250-300ms where the data propagates through multiple cloud service. In conclusion, this report illustrates the feasibility of merging two major platforms together: ROS and AWS IoT, and moreover, the accessibility to exploit the power and potential enabled by the modern data centers. / Avhandlingens syfte är att utforma och utveckla en plattform baserat på konceptet Internet of Robotic Things konstruerat av en robotikplattform, en Internet of Things plattform och molntjänster. En Internet of Things plattform är ett globalt nätverk som tillåter många enheter att kommunicera med varandra och överföra data över Internet. En robotikplattform underlättar kontrollen av in/ut mellan mjukvara, mekaniska enheter och elektroniska system. Molntjänster är en gemensam pool av skalbar hårdvara som vanligtvis erbjuds av tredje parts molnleverantörer. En Internet of Robotic Things plattform är en global infrastruktur som underlättar avancerade robotar att interagera över Internet genom en gemensam kommunikationsteknik, en pool av molntjänster som delas av alla uppkopplade robotar som tillåter skalbar lagring och processorkraft.Plattformens huvudkomponenter är robotikplattformen Robot Operating System, Internet of Things plattformen AWS IoT Core och molntjänsterna Amazon DynamoDB och AWS Lambda för lagring och databearbetning.Plattformen evalueras i form av plattformegenskaperna, fördröjningar & funktionstid och visualiseringsförmåga. Plattformen visar lovande resultat i from av fördröjningar mellan två robotar som utbyter data med hjälp av IoT plattformen, där fördröjningarna är begränsade av distanssträckan. Plattformens egenskap att visualisera strömmande data från robotar möjliggör för en operatör att visualisera utvald data från plattformen över internet i realtid.

In recent years, the interest and utilization of VPNs and cloud computing have surged, which has led to the development of Cloud VPNs. Cloud VPNs are often used to give employers access to company resources over a secured and trusted network, but individuals can also use them. Cloud computing offers numerous advantages, including cost efficiency, scalability, security, and reliability. Companies and individuals widely use it to streamline operations, reduce expenditures, and leverage technologies without the need to maintain on-premise infrastructure. Cloud-based solutions impose specific requirements on technologies designed to operate in the cloud, which include security, efficient resource management, and high network availability and performance. In this thesis, we will analyze and evaluate the network performance impact that Amazon Web Services (AWS) and Microsoft Azure, two of the market-leading cloud platforms, have on the VPN services OpenVPN and WireGuard while considering associated operational costs and user complexity. The network performance impact is evaluated through measuring the throughput, latency, jitter, and packet loss. We have performed experiments divided into three setups, each simulating different traffic patterns, and VPN usage scenarios. Throughout these experiments, we observed and documented the user complexity related to setup, installation, and configuration processes. Our findings indicate that Azure has the best overall network throughput across all setups, fewer retransmissions, and fewer packet losses. Conversely, AWS exhibits lower latency and jitter. Additionally, our assessment of operational costs and user complexity reveals that Azure offers lower associated costs but a higher user complexity. Furthermore, our experiments identified that WireGuard, when paired with Azure, offers the best VPN solution.

The thesis is aimed on construction of solar panel powered datalogger of environmental values. The thesis begins by research of commercial dataloggers. Then the concept of datalogger was created. Then the electronics of datalogger was designed. Datalogger is built as a system driven by microcontroller. The microcontroller reads data from sensors, writes data to memory card and sends data to Czech radiocommunications cloud by LoRa technology. Then the data are resend to MySQL database. Program for microcontroller was made in C language. Configuration of datalogger is done through USB interface by PC application which was created in C# language. For visualisation of measured data ASP.NET Core application was created.

Cette thèse a pour but l'enrichissement des méthodes supervisées d'analyse de données symboliques et l'extension de ce domaine aux données volumineuses, dites "Big Data". Nous proposons à cette fin une méthode supervisée nommée HistSyr. HistSyr convertit automatiquement les variables continues en histogrammes les plus discriminants pour les classes d'individus. Nous proposons également une nouvelle méthode d'arbres de décision symbolique, dite SyrTree. SyrTree accepte tous plusieurs types de variables explicatives et à expliquer pour construire l'arbre de décision symbolique. Enfin, nous étendons HistSyr aux Big Data, en définissant une méthode distribuée nommée CloudHistSyr. CloudHistSyr utilise Map/Reduce pour créer les histogrammes les plus discriminants pour des données trop volumineuses pour HistSyr. Nous avons testé CloudHistSyr sur Amazon Web Services (AWS). Nous démontrons la scalabilité et l’efficacité de notre méthode sur des données simulées et sur les données expérimentales. Nous concluons sur l’utilité de CloudHistSyr qui , grâce à ses résultats, permet l'étude de données massives en utilisant les méthodes d'analyse symboliques existantes. / This Thesis proposes new supervised methods for Symbolic Data Analysis (SDA) and extends this domain to Big Data. We start by creating a supervised method called HistSyr that converts automatically continuous variables to the most discriminant histograms for classes of individuals. We also propose a new method of symbolic decision trees that we call SyrTree. SyrTree accepts many types of inputs and target variables and can use all symbolic variables describing the target to construct the decision tree. Finally, we extend HistSyr to Big Data, by creating a distributed method called CloudHistSyr. Using the Map/Reduce framework, CloudHistSyr creates of the most discriminant histograms for data too big for HistSyr. We tested CloudHistSyr on Amazon Web Services. We show the efficiency of our method on simulated data and on actual car traffic data in Nantes. We conclude on overall utility of CloudHistSyr which, through its results, allows the study of massive data using existing symbolic analysis methods.

Attack simulations provide a viable means to test the cyber security of a system. The simulations trace the steps taken by the attacker to compromise sensitive assets within the system. In addition to this, they can also estimate the time taken by the attacker for the same, measuring from the initial step up to the final. One common approach to implement such simulations is the use of attack graph, which trace the various dependencies of every step and their connection to one another in a formal way. To further facilitate attack simulations and to reduce the effort of creating new attack graphs for each system of a given type, domain-specific languages are employed. Another advantage of utilizing such a language is that they organize the common attack logics of the domain in a systematic way, allowing for both ease of use and reuse of models. MAL (the Meta Attack Language) has been proposed by Johnson et al. to serve as a framework to develop domain-specific languages [1]. My work is based upon the same. This thesis report presents AWSLang, which can be used to design IT system models in context to the AWS (Amazon Web Services) environment and analyse their weaknesses. The domain specifics of the language are inspired from and based on existing literature. A Systematic Literature Review (SLR) is performed to identify possible attacks against the elements in an AWS environment. These attacks are then used as groundwork to write test cases and validate the specification. / Attacksimuleringar är ett användbart sätt att testa cybersäkerheten i ett system. Simuleringarna spårar de steg som angriparen tog för att försvaga säkerheten av känsliga tillgångar inom systemet. Utöver detta kan de uppskatta hur länge attacken varade, mätt från första till sista steget. Ett gemensamt tillvägagångssätt för att implementera sådana simuleringar är användningen av attackgrafer, som spårar olika beroenden av varje steg och deras koppling till varandra på ett formellt sätt.För att ytterligare underlätta attacksimuleringar och minska ansträngningen att skapa nya attackgrafer för varje system av en given typ, används domänspecifika språk. En annan fördel med att använda ett sådant språk är att det organiserar domänens gemensamma attacklogiker på ett systematiskt sätt, vilket möjliggör både en enkel användning och återanvändning av modeller. MAL (Meta Attack Language) har föreslagits av Johnson et al. att fungera som ramverk för utvecklingen av domänspecifika språk [1]. Mitt arbete är baserat på detsamma.I denna uppsats presenteras AWSLang, som kan användas för att utforma IT-systemmodeller i kontexten av AWS-miljön (Amazon Web Services) och analysera deras svagheter. Språkets domänspecifikationer är inspirerade av och baserade på befintlig litteratur. En systematisk litteraturöversikt görs för att identifiera möjliga attacker mot elementen i en AWS-miljö. Dessa attacker används sedan som grund för att skriva testfall och validera specifikationen.