An understanding of opportunities and challenges in cloud computing is needed to better manage technology costs and create financial value. The purposes of this transcendental phenomenological study were to understand the lived experiences of minority business owners who operated business applications in the cloud and to explore how these experiences created financial value for businesses despite security challenges. Historically, minority business owners have experienced high rates of business failures and could benefit from information to help them manage business costs in order to position their businesses to grow and succeed. Modigliani-Miller's theorem on capital structure and Brealey and Young's concept of financial leverage were the conceptual frameworks that grounded this study. Data consisted of observational field notes and 15 individual semistructured interviews with open-ended questions. I used the in vivo and pattern coding approaches to analyze the data for emerging themes that addressed the research questions. The findings were that drivers of positive cloud-based experiences, such as easy access, ease of use, flexibility, and timesavings, created financial value for small business owners. In addition, the findings confirmed that opportunities in the cloud such as cost savings, efficiency, and ease of collaboration outweighed security challenges. Finally, the results indicated that cost-effective approaches such as the subscription model for acquiring technology created financial value for businesses. The findings of this study can be used by business owners, especially minority small business owners, to decide whether to move operations to the cloud to create financial value for their businesses.

Industrial experts agree that cloud computing can significantly improve business and public access to low cost computing power and storage. Despite the benefits of cloud computing, recent research surveys indicated that its adoption in U.S. hospitals is slower than expected. The purpose of this study was to understand what factors influence cloud adoption in U.S. hospitals. The theoretical foundation of the research was the diffusion of innovations and technology-organization-environment framework. The research question was to examine the predictability of cloud computing adoption for U.S. hospitals as a function of 6 influential factors: relative advantage, compatibility, complexity, organizational size, structure, and culture. The research methodology included a cross-sectional survey with an existing validated questionnaire. A stratified random sample of 118 information technology managers from qualified U.S. hospitals completed the questionnaire. The categorical regression analysis rendered F statistics and R2 values to test the predictive models. The research results revealed that all 6 influential factors had significant correlations with the public cloud adoption intent (adjusted R2 = .583) while only the 3 technological factors had significant correlations with the private cloud adoption intent (adjusted R2 = .785). The recommendation is to include environmental factors and increase sample size in the similar future research. The developed predictive models provided a clearer understanding among hospital IT executives and cloud service providers of cloud adoption drivers. The potential implications for positive social change can be the increase of efficiency and effectiveness in U.S. hospital operation once their speed of cloud adoption has increased.

Šio darbo pagrindinis tikslas yra suprojektuoti ir realizuoti debesų kompiuterijos paslaugą internetinei matematinio programavimo ir modeliavimo sistemai, kuri galėtų pakeisti šios sistemos kompiliavimo ir vykdymo serverinę dalį. Šiam tikslui pasiekti buvo nagrinėjama debesies kompiuterijos samprata. Atlikta debesies platformų analizė bei jų palyginimas tarpusavyje. Taip pat apžvelgti debesų kompiuterijos paslaugų tipai. Identifikuojamos ir nagrinėjamos problemos kurios iškilo projektuojant ir realizuojant debesų kompiuterijos paslaugą. Sukurta debesų kompiuterijos paslauga galinti pakeisti šio metu internetinėje matematinio programavimo ir modeliavimo sistemoje naudojama kompiliavimo ir vykdymo modulį. / The purpose of this work is to develop Cloud Computing service for Online Mathematical Programming and Simulation system, which can replace currently used compilation and execution server. To achieve this goal there was analysed Cloud Computing concept. Also Cloud Computing service types were reviewed. Moreover Cloud platforms there were analysed and compared to each other. Identified and analysed problems, which were raised in development process. Also was developed Cloud Computing Service which can replace currently used compiling and execution module in Online Mathematical Programming and Simulation system.

Made available in DSpace on 2016-12-12T20:22:52Z (GMT). No. of bitstreams: 1 Gustavo Andriolli S Cavalcanti.pdf: 1327760 bytes, checksum: bc2279bc162dd158d8e1d52a2a7df585 (MD5) Previous issue date: 2014-07-11 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Virtual infrastructures (VIs) sets of virtual machines interconnected by links and routers also virtual consolidated the dynamic provisioning of computing and communication resources and their services providers (InPs) face a challenge in choose the better approach to allocating and reserve these resources. Resource allocation (such as CPU, disk, memory, bandwidth) is a complex problem that needs to satisfy different goals: users expect to run their applications on survivable VIs, while InPs aim to maximize profits, minimize costs and reduce substrate fragmentation. However, there is a dichotomy between maximizing VI survivability, by sparsely allocating resources to decrease the impact of substrate failures, and minimizing substrate fragmentation, by co-locating VIs. In this context, we propose a mixed integer programming model to allocate resources considering the joint coordination of survivability and fragmentation. Experimental results show that it is possible to enhance VI survivability without significantly impacting substrate fragmentation. / Com a consolidação do provisionamento dinâmico de Infraestruturas Virtuais (IVs) - conjuntos de máquinas virtuais interconectadas por enlaces e roteadores também virtuais -, provedores de serviço (InPs) enfrentam um desafio na escolha da melhor abordagem para alocação e reserva de recursos computacionais e de comunicação. ´E fato que a alocação de recursos (como CPU, disco, memória, largura de banda) é um problema complexo que precisa satisfazer diferentes objetivos: usuários esperam executar suas aplicações em IVs eficientes e confiáveis, enquanto InPs objetivam maximizar lucros, minimizar custos e reduzir a fragmentação do substrato físico. Sobretudo, há uma dicotomia entre maximizar a confiabilidade de IVs, alocando recursos esparsos para diminuir o impacto de falhas no substrato, e minimizara fragmentação do substrato, co-alocando IVs. Nesse contexto, ´e proposto um modelo de programação inteira mista para alocar recursos considerando a coordenação conjunta de confiabilidade e fragmentação. Resultados experimentais mostram que é possível aprimorar a confiabilidade de IVs sem impactar significativamente na fragmentação do substrato.

The Magellanic Clouds (MCs) consist of a pair of galaxies, the Large Magellanic Cloud (LMC) and the Small Magellanic Cloud (SMC), which are located at a distance of 50 kpc and 60 kpc, with stellar masses of 1010 M and 109 M , respectively. Morphologically they are categorized as irregular type galaxies. The MCs are gas rich and metal poor (Z=0.008 for LMC, and 0.004 for SMC) as compared to the Milky Way (MW), and have active star-forming regions. Their proximity and location at high galactic latitude enable us to resolve their individual populations as well as detect faint stellar populations. It is well known that the MCs are interacting with each other, as well as with the MW. The interaction is supported by the presence of the Magellanic Bridge and the Magellanic Stream. The evolved stellar populations in the MCs help us to understand their evolution and interaction process. The MCs host both Population I as well as Population II stars. This extended range of star formation is a valuable source of information to understand the formation and evolution of galaxies in general, and the MCs in particular. Evolved stellar popu-lation means the stars that have evolved o the main sequence and the giants, such as red giants (RGs), red clump stars, and asymptotic giant branch stars. There is a dominant population of evolved stars present in the MCs, in star clusters as well as in the eld. The aim of the thesis is to study the evolved stellar populations for one of the component of the MCs, the LMC. The study is primarily divided into two parts. (1) Study of sparse star clusters in the LMC: To increase our understanding of sparse star clusters in the LMC, with well estimated parameters, using deep Washington photometric data for 45 LMC clusters. (2) To estimate a metallicity map of LMC: In order to understand the metallicity variation across the galaxy. This is done by creating a high spatial resolution metallicity map of the LMC, using red giant branch (RGB) stars, with the help of photometric data and calibrated using spectroscopic studies of RGs in eld and star clusters. The introduction to the thesis study along with the aim are described in Chapter 1 of the thesis. The three sets of photometric data used for this study are described in Chapter 2. The data sets are: CT1 Washington photometric data for 45 star clusters within the LMC, the VI photometric data from the Optical Gravitational Lensing Experiment Phase-III survey (OGLE III), and the Magellanic Cloud Photometric Survey (MCPS). Study of sparse star clusters in the LMC: A systematic study is per-formed to analyse the 45 cluster candidates, to estimate their parameters (radius, reddening, and age) using the main-sequence turn-o (MSTO), as well as the evolved portion of the colour{magnitude diagram (CMD). The basic parameters were estimated for 33 genuine clusters, whereas the other 12 cluster candidates have been classi ed as possible clusters/asterisms. The study of 33 star clusters are presented in Chapter 3. These clus-ters are categorized as genuine star clusters based on their strong density enhancement and cluster features with respect to their surrounding eld regions. Out of the 33 clusters, 23 are identi ed as single clusters and 10 are found to be members of double clusters. Detailed discussions of all the individual clusters are presented. The estimated parameters for the single and double clusters are listed in two di erent tables. About 50% of the clusters are in the age range 100{300 Myr, the rest of them being older or younger. Comparison with previous age estimates shows some agreement as well as some deviation. The remaining 12 clusters which could not be categorized as genuine star clusters are studied in Chapter 4. These clusters have poor (/suspi-cious) density enhancement and cluster features when compared to their surrounding elds. It is important to study such cluster candidates, as these objects probe the lower limit of the cluster mass function. Detailed discussion on these individual objects are presented and their estimated parameters are tabulated in this chapter. A detailed discussion based on the study of all the 45 inconspicuous clusters is presented in this chapter, including the estimated sizes (radii 2{10 pc), reddening with respect to eld, and location in the LMC. The mass limit estimated for genuine clusters is found to be 1000 M , whereas for possible clusters/asterisms it is few 100 M , using synthetic CMDs. The study of sparse clusters enlarged the number of objects con rmed as genuine star clusters (33) and estimated their fundamental parameters. The study emphasizes that the sizes and masses of the studied sample are found to be similar to that of open clusters in the MW. Thus, this study adds to the lower end of cluster mass distribution in the LMC, suggesting that the LMC, apart from hosting rich clusters, also has formed small, less massive open clusters in the 100{300 Myr age range. The 12 cases of possible clusters/asterisms are worthy of attention, in the sense that they can throw light on the survival time of such objects in the LMC. Photometric metallicity map of the LMC using RGB stars: A metallic-ity map of the LMC is estimated using OGLE III and MCPS photometric data. This is a rst of its kind map of metallicity up to a radius of 4{5 de-grees, derived using photometric data and calibrated using spectroscopic data of RGB stars. The RGB is identi ed in the V, (V I) CMDs of small areal subregions of varying sizes in both data sets. The slope of the RGB is used as an indicator of the average metallicity of a subregion, and this RGB slope is calibrated to metallicity using spectroscopic data for eld and cluster RGs in selected subregions. The metallicity map estimated using OGLE III photometric data is presented in Chapter 5. A method to identify the RGB of small subre-gions within the LMC and estimate its slope by using a consistent and automated method was developed. The technique is robust and indepen-dent of reddening and extinction. The details of calibrating the RGB slopes to metallicities, using previous spectroscopic results of RGs in eld and star clusters are presented. The OGLE III metallicity maps are pre sented, based on four cut-o criteria to separate regions with good ts. The OGLE III map has substantial coverage of the bar, the eastern and western LMC, but does not cover the northern and southern regions. The OGLE III metallicity map shows the bar region to be metal rich whereas the eastern and western regions to be relatively metal poor. The mean metallicity is estimated for three di erent regions within the LMC. For the complete LMC the mean [Fe/H] is = 0.39 dex ( [Fe/H] = 0.10); for the bar region it is = 0.35 dex ( [Fe/H] = 0.9); and for the outer LMC it is = 0.46 dex ( [Fe/H] = 0.11). The metallicity histogram for these di erent regions are also estimated. A radial metallicity gradient is estimated in the de-projected plane of the LMC. The metallicity gradient is seen to remain almost constant in the bar region (till a radius of 2.5 kpc) and has a shallow gradient of 0.066 0.006 dex kpc 1 beyond that till 4 kpc. In Chapter 6 the metallicity map based on MCPS photometric data is estimated. The MCPS data covers more of the northern and south-ern LMC (less of eastern and western regions) and is important to be analysed in order to reveal the metallicity trend of the overall disk. The systematic di erences between the lter systems of MCPS and OGLE III are corrected, and the MCPS slopes are then calibrated using the OGLE III slope{metallicity relation. The MCPS metallicity maps are presented, based on four cut-o criteria to separate regions with good ts. The bar region is found to be metal rich as was found using OGLE III data, whereas the northern and southern regions are marginally metal poor. The mean metallicity estimated for the complete LMC is = 0.37 dex ( [Fe/H] = 0.12); and for the outer LMC it is = 0.41 dex ( [Fe/H] = 0.11). The metallicity histogram for these di erent regions are estimated and compared with the OGLE III distribution. The metallicity range of the complete LMC is found to be almost similar for both data sets. The metallicity distribution within the bar has a narrow range as found using both data sets. The slight di erence between mean metallicity of outer LMC for the two data sets is attributed to their coverage. We suggest that the northern and southern regions of the LMC could be marginally more metal rich than the eastern and western regions. The metallicity gradient of the LMC disk, estimated from MCPS data is found to be shallow 0.049 0.002 dex kpc 1 till about 4 kpc. We also constructed a metallicity map of outliers using both OGLE III and MCPS data, and identi ed subregions where the mean metallic-ity di ers from the surrounding areas. We suggest further spectroscopic studies in order to assess their physical significance. The detailed conclusion of the thesis and future work are presented in Chapter 7. From the study of sparse star clusters in the LMC, it is concluded that LMC has open cluster like star cluster systems. It is important to include them to understand the cluster formation history (CFH) and their survival time scale. Presently, our understanding of the CFH is dominated by rich clusters. The bar of the LMC is found to be the most metal rich region, and the LMC metallicity gradient though shallow, resembles the gradient seen in spiral galaxies. The gradient is also similar to that found in our Galaxy. The higher metallicity in the bar region might indicate an active bar in the past.

Les applications logicielles sont de plus en plus diversifié et complexe. Avec le développement orageux du Cloud Computing et de ses applications, les applications logicielles deviennent encore plus complexes que jamais. Les applications de cloud computing complexes peuvent contenir un grand nombre de composants logiciels qui nécessitent et consomment une grande quantité de ressources (matériel ou d'autres composants logiciels) répartis en plusieurs niveaux en fonction de la granularité de ces ressources. En outre, ces composants logiciels peuvent être situés sur différents nuages. Les composants logiciels et de leurs ressources requises d'une application Nuage ont des relations complexes dont certains pourraient être résolus au moment de la conception, mais certains sont nécessaires pour faire face au moment de l'exécution. La complexité des logiciels et de l'hétérogénéité de l'environnement Couverture devenir défis que les solutions d'élasticité actuelles ont besoin de trouver des réponses appropriées à résoudre. L'élasticité est l'un des avantages du cloud computing, qui est la capacité d'un système Cloud pour adapter à la charge de travail des changements par des ressources d'approvisionnement et deprovisioning d'une manière autonome. Par conséquent, les ressources disponibles correspondent à la demande actuelle d'aussi près que possible à chaque moment. Pour avoir une solution d'élasticité efficace, qui ne reflète pas seulement la complexité des applications Cloud mais également à déployer et à gérer eux d'une manière autonome, nous proposons une approche d'élasticité roman. Il est appelé à plusieurs niveaux élasticité fine qui comprend deux aspects de la complexité de l'application: plusieurs composants logiciels et la granularité des ressources. Le multi-niveau élasticité fine concerne les objets touchés par les actions d'élasticité et la granularité de ces actions. Dans cette thèse, nous introduisons plateforme Roboconf un système de cloud computing autonome (ACCS) pour installer et reconfigurer les applications complexes ainsi que soutenir le multi-niveau élasticité fine. A cet effet, Roboconf est également un gestionnaire d'élasticité autonome. Merci à cette plate-forme, nous pouvons abstraire les applications cloud complexes et automatiser leur installation et de reconfiguration qui peut être de plusieurs centaines d'heures de travail. Nous utilisons également Roboconf à mettre en œuvre les algorithmes de multi-niveau élasticité fine sur ces applications. Les expériences menées indiquent non seulement l'efficacité de l'élasticité fine multi-niveau, mais aussi de valider les caractéristiques de support de cette approche de la plateforme Roboconf. / Software applications are becoming more diverse and complex. With the stormy development of Cloud Computing and its applications, software applications become even more complex than ever. The complex Cloud applications may contain a lot of software components that require and consume a large amount of resources (hardware or other software components) distributed into multiple levels based on granularity of these resources. Moreover these software components might be located on different clouds. The software components and their required resources of a Cloud application have complex relationships which some could be resolved at design time but some are required to tackle at run time. The complexity of software and heterogeneity of Cloud environment become challenges that current elasticity solutions need to find appropriate answers to resolve. Elasticity is one of benefits of Cloud computing, which is capability of a Cloud system to adapt to workload changes by provisioning and deprovisioning resources in an autonomic manner. Hence, the available resources fit the current demand as closely as possible at each point in time. To have an efficient elasticity solution which not only reflects the complexity of Cloud applications but also deploy and manage them in an autonomic manner, we propose a novel elasticity approach. It is called multi-level fine-grained elasticity which includes two aspects of application’s complexity: multiple software components and the granularity of resources. The multi-level fine-grained elasticity concerns objects impacted by elasticity actions and granularity of these actions. In this thesis, we introduce Roboconf platform an autonomic Cloud computing system (ACCS) to install and reconfigure the complex applications as well as support the multi-level fine-grained elasticity. To this end, Roboconf is also an autonomic elasticity manager. Thanks to this platform, we can abstract the complex Cloud applications and automate their installation and reconfiguration that can be up to several hundred hours of labour. We also use Roboconf to implement the algorithms of multi-level fine-grained elasticity on these applications. The conducted experiments not only indicate efficiency of the multi-level fine-grained elasticity but also validate features supporting this approach of Roboconf platform.

Cette thèse porte sur le paradigme « Mobile Edge cloud» qui rapproche le cloud des utilisateurs mobiles et qui déploie une architecture de clouds locaux dans les terminaisons du réseau. Les utilisateurs mobiles peuvent désormais décharger leurs tâches de calcul pour qu’elles soient exécutées par les femto-cellules (FCs) dotées de capacités de calcul et de stockage. Nous proposons ainsi un concept de regroupement de FCs dans des clusters de calculs qui participeront aux calculs des tâches déchargées. A cet effet, nous proposons, dans un premier temps, un algorithme de décision de déportation de tâches vers le cloud, nommé SM-POD. Cet algorithme prend en compte les caractéristiques des tâches de calculs, des ressources de l’équipement mobile, et de la qualité des liens de transmission. SM-POD consiste en une série de classifications successives aboutissant à une décision de calcul local, ou de déportation de l’exécution dans le cloud.Dans un deuxième temps, nous abordons le problème de formation de clusters de calcul à mono-utilisateur et à utilisateurs multiples. Nous formulons le problème d’optimisation relatif qui considère l’allocation conjointe des ressources de calculs et de communication, et la distribution de la charge de calcul sur les FCs participant au cluster. Nous proposons également une stratégie d’éparpillement, dans laquelle l’efficacité énergétique du système est améliorée au prix de la latence de calcul. Dans le cas d’utilisateurs multiples, le problème d’optimisation d’allocation conjointe de ressources n’est pas convexe. Afin de le résoudre, nous proposons une reformulation convexe du problème équivalente à la première puis nous proposons deux algorithmes heuristiques dans le but d’avoir un algorithme de formation de cluster à complexité réduite. L’idée principale du premier est l’ordonnancement des tâches de calculs sur les FCs qui les reçoivent. Les ressources de calculs sont ainsi allouées localement au niveau de la FC. Les tâches ne pouvant pas être exécutées sont, quant à elles, envoyées à une unité de contrôle (SCM) responsable de la formation des clusters de calculs et de leur exécution. Le second algorithme proposé est itératif et consiste en une formation de cluster au niveau des FCs ne tenant pas compte de la présence d’autres demandes de calculs dans le réseau. Les propositions de cluster sont envoyées au SCM qui évalue la distribution des charges sur les différentes FCs. Le SCM signale tout abus de charges pour que les FCs redistribuent leur excès dans des cellules moins chargées.Dans la dernière partie de la thèse, nous proposons un nouveau concept de mise en cache des calculs dans l’Edge cloud. Afin de réduire la latence et la consommation énergétique des clusters de calculs, nous proposons la mise en cache de calculs populaires pour empêcher leur réexécution. Ici, notre contribution est double : d’abord, nous proposons un algorithme de mise en cache basé, non seulement sur la popularité des tâches de calculs, mais aussi sur les tailles et les capacités de calculs demandés, et la connectivité des FCs dans le réseau. L’algorithme proposé identifie les tâches aboutissant à des économies d’énergie et de temps plus importantes lorsqu’elles sont téléchargées d’un cache au lieu d’être recalculées. Nous proposons ensuite d’exploiter la relation entre la popularité des tâches et la probabilité de leur mise en cache, pour localiser les emplacements potentiels de leurs copies. La méthode proposée est basée sur ces emplacements, et permet de former des clusters de recherche de taille réduite tout en garantissant de retrouver une copie en cache. / Mobile Edge Cloud brings the cloud closer to mobile users by moving the cloud computational efforts from the internet to the mobile edge. We adopt a local mobile edge cloud computing architecture, where small cells are empowered with computational and storage capacities. Mobile users’ offloaded computational tasks are executed at the cloud-enabled small cells. We propose the concept of small cells clustering for mobile edge computing, where small cells cooperate in order to execute offloaded computational tasks. A first contribution of this thesis is the design of a multi-parameter computation offloading decision algorithm, SM-POD. The proposed algorithm consists of a series of low complexity successive and nested classifications of computational tasks at the mobile side, leading to local computation, or offloading to the cloud. To reach the offloading decision, SM-POD jointly considers computational tasks, handsets, and communication channel parameters. In the second part of this thesis, we tackle the problem of small cell clusters set up for mobile edge cloud computing for both single-user and multi-user cases. The clustering problem is formulated as an optimization that jointly optimizes the computational and communication resource allocation, and the computational load distribution on the small cells participating in the computation cluster. We propose a cluster sparsification strategy, where we trade cluster latency for higher system energy efficiency. In the multi-user case, the optimization problem is not convex. In order to compute a clustering solution, we propose a convex reformulation of the problem, and we prove that both problems are equivalent. With the goal of finding a lower complexity clustering solution, we propose two heuristic small cells clustering algorithms. The first algorithm is based on resource allocation on the serving small cells where tasks are received, as a first step. Then, in a second step, unserved tasks are sent to a small cell managing unit (SCM) that sets up computational clusters for the execution of these tasks. The main idea of this algorithm is task scheduling at both serving small cells, and SCM sides for higher resource allocation efficiency. The second proposed heuristic is an iterative approach in which serving small cells compute their desired clusters, without considering the presence of other users, and send their cluster parameters to the SCM. SCM then checks for excess of resource allocation at any of the network small cells. SCM reports any load excess to serving small cells that re-distribute this load on less loaded small cells. In the final part of this thesis, we propose the concept of computation caching for edge cloud computing. With the aim of reducing the edge cloud computing latency and energy consumption, we propose caching popular computational tasks for preventing their re-execution. Our contribution here is two-fold: first, we propose a caching algorithm that is based on requests popularity, computation size, required computational capacity, and small cells connectivity. This algorithm identifies requests that, if cached and downloaded instead of being re-computed, will increase the computation caching energy and latency savings. Second, we propose a method for setting up a search small cells cluster for finding a cached copy of the requests computation. The clustering policy exploits the relationship between tasks popularity and their probability of being cached, in order to identify possible locations of the cached copy. The proposed method reduces the search cluster size while guaranteeing a minimum cache hit probability.

The emergence of cloud computing has transformed the potential of robotics by enabling multi-robotic teams to fulfil complex tasks in the cloud. This paradigm is known as “cloud robotics” and relieves robots from hardware and software limitations, as large amounts of available resources and parallel computing capabilities are available in the cloud. The introduction of cloud-enabled robots alleviates the need for computationally intensive robots to be built, as many, if not all, of the CPU-intensive tasks can be offloaded into the cloud, resulting in multi-robots that require much less power, energy consumption and on-board processing units. While the benefits of cloud robotics are clearly evident and have resulted in an increase in interest among the scientific community, one of the biggest challenges of cloud robotics is the inherent communication challenges brought about by disconnections between the multi-robotic system and the cloud. The communication delays brought about by the cloud disconnection results in robots not being able to receive and transmit data to the physical cloud. The unavailability of these robotic services in certain instances could prove fatal in a heterogeneous environment that requires multi-robotic teams to assist with the saving of human lives. This niche area is relatively unexplored in the literature. This work serves to assist with the challenge of disconnection in cloud robotics by proposing a survivable cloud multi-robotics (SCMR) framework for heterogeneous environments. The SCMR framework leverages the combination of a virtual ad hoc network formed by the robot-to-robot communication and a physical cloud infrastructure formed by the robot-to-cloud communications. The Quality of Service (QoS) on the SCMR framework is tested and validated by determining the optimal energy utilization and Time of Response (ToR) on drivability analysis with and without cloud connection. The experimental results demonstrate that the proposed framework is feasible for current multi-robotic applications and shows the survivability aspect of the framework in instances of cloud disconnection. / School of Computing / M.Sc. (Computer Science)

No contexto da Engenharia de Software para a Computação em Nuvem as pesquisas relacionadas ao tema são cada vez mais crescentes e investiga-se como a Computação em Nuvem influenciará no desenvolvimento de sistemas de uma maneira geral. A atividade de construir sistemas para nuvem é uma tarefa complexa, criar aplicações de múltiplas nuvens, sobretudo, no contexto do modelo de serviço Plataforma-como-um-Serviço(PaaS), é ainda mais agravada devido especificidades de plataformas de nuvem que podem tornar a tarefa de desenvolvimento repetitiva, custosa e dependente de um provedor específico. As abordagens dirigidas por modelos(MDE) resolvem alguns desses problemas, elas propõem que a modelagem e mecanismos de transformação utilizados para gerar código a partir de modelos são uma melhor maneira de desenvolver sistemas de software, ao invés da codificação pura. Portanto, visando investigar como combinar os benefícios da Computação em Nuvem alinhados ao MDE, foi desenvolvida uma abordagem dirigida por modelos para desenvolvimento de aplicações multi-PaaS. Em direção a este objetivo foi realizado um Estudo de Caso em colaboração com uma empresa da indústria. Essa colaboração permitiu a criação de implementações de referencia que possibilitaram o desenvolvimento de uma Linguagem Específica de Domínio (DSL) e metaprogramas que compõem a abordagem. Para avaliar a abordagem desenvolvida foi realizado um Estudo de Caso. Os resultados mostram que MDE pode não só resolver o problema, mas trazer benefícios adicionais em relação a abordagens tradicionais de desenvolvimento de sistemas. Este trabalho explora esses benefícios, apresenta uma maneira de unir recursos heterogêneos de nuvem por meio de uma abordagem dirigida por modelos e aplicações orientadas a serviço. / Cloud computing is a computational paradigm that has increasingly been used in various sectors of industry and academia. Researchers have been studying how cloud technologies can influence several areas of science and research. In the context of Software Engineering, the researches related to cloud are increasingly increasing. Researchers are studying how to develop better cloud services offerings and how to find a strategy for combining existing resources to build improved services and solve problems. Building cloud systems is a complex task, in the context of the Platform-as-a-Service(PaaS) cloud service model; this activity is further aggravated by cloud platform specificities that can make the task of development repetitive, costly,and platform-specific. Model-driven approaches (MDE) solve some of these issues; they propose that the modeling and transformation mechanisms used to generate code from models are a better way to develop software systems, rather than pure coding. Development with MDE is a comprehensive and relevant research area and needs to be better explored in a wide range of contexts. Therefore, in order to investigate how to combine the benefits of multi-cloud appications aligned to the MDE, we developed a model-driven approach to build multi-PaaS applications.Toward this objective, we performed a case study in collaboration with an industry company.This collaboration allowed the creation of reference implementations that enabled the development of a Domain Specific Language (DSL) and metaprograms that constitute the approach. To evaluate the approach, we performed a case study. The results show that MDE cannot only solve the problem, but also bring additional benefits over traditional systems development approaches. This work explores these benefits, presents a way to combine heterogeneous cloud resources through a service-driven model and application-driven approach.

Vi lever i ett informationssamhälle. Idag hittar vi fakta om vad som helst, var vi än är, genom de gigantiska och otroliga resurser som nås via ICT idag. Med ökad teknisk kapacitet har informationsinsamlingen ökat drastiskt i mängd. Hur vi lagrar data är dock mindre diskuterat. Det nya skifte som kommit fram i datalagring är en flytt från lokalt förvaltade servrar till lagring i molnet. Molntjänster är ett idag allmänt känt begrepp inom IT-branschen, men för att få så mycket uppmärksamhet är termen väldigt vag. Uppsatsen syftar till att reflektera kring molnets innebörd samt utreda om molnet lämpas för datalagring. Studien fastställer vilken affärsnytta en organisation kan utvinna av datalagring i molnet samt om de problem som finns det datalagring idag kan lösas genom att flytta lagringen till molnet. De problem som kommer med den ökade datamängden i informationssamhället samt de lagringstekniker i form av servrar som används idag, presenteras. Teoretiska definitioner av molnet framställs samt hur dessa definitioner skiljer sig från användares synsätt. Resultatet som presenteras ger en definition av hur molntjänster kan användas av organisationer för att lagra data samt vad organisationer kan utvinna av detta. / Today’s information society brings big changes in how we gather information. During the last couple of years the amount of information we store has increased substantially. Techniques for gathering information develops fast, but the way we store information stays the same. This study looks in to cloud computing and if storing data in the cloud is a good alternative to local storage. The definition of cloud computing is vague, and this study attempts to give an account for what cloud computing is through theories and user definitions. Problems with the amount of increased data, and server techniques we use today are presented. The study confirms business advantages with storing data in the cloud and give clearance to that the cloud is working alternative to local storage.