Nos últimos anos, Cloud Computing tem se tornado uma tecnologia importante que possibilitou executar aplicações sem a necessidade de implementar uma infraestrutura física com a vantagem de reduzir os custos ao usuário cobrando somente pelos recursos computacionais utilizados pela aplicação. O desafio com a implementação de aplicações distribuídas em ambientes de Cloud Computing é o planejamento da infraestrutura de máquinas virtuais visando otimizar o tempo de execução e o custo da implementação. Assim mesmo, nos últimos anos temos visto como a quantidade de dados produzida pelas aplicações cresceu mais que nunca. Estes dados contêm informação valiosa que deve ser obtida utilizando ferramentas como MapReduce. MapReduce é um importante framework para análise de grandes quantidades de dados desde que foi proposto pela Google, e disponibilizado Open Source pela Apache com a sua implementação Hadoop. O objetivo deste trabalho é apresentar que é possível predizer o tempo de execução de uma aplicação distribuída, a saber, uma aplicação MapReduce, na infraestrutura de Cloud Computing, utilizando um modelo matemático baseado em especificações teóricas. Após medir o tempo levado na execução da aplicação e variando os parámetros indicados no modelo matemático, e, após utilizar uma técnica de regressão linear, o objetivo é atingido encontrando um modelo do tempo de execução que foi posteriormente aplicado para predizer o tempo de execução de aplicações MapReduce com resultados satisfatórios. Os experimentos foram realizados em diferentes configurações: a saber, executando diferentes aplicações MapReduce em clusters privados e públicos, bem como em infraestruturas de Cloud comercial, e variando o número de nós que compõem o cluster, e o tamanho do workload dado à aplicação. Os experimentos mostraram uma clara relação com o modelo teórico, indicando que o modelo é, de fato, capaz de predizer o tempo de execução de aplicações MapReduce. O modelo desenvolvido é genérico, o que quer dizer que utiliza abstrações teóricas para a capacidade computacional do ambiente e o custo computacional da aplicação MapReduce. Motiva-se a desenvolver trabalhos futuros para estender esta abordagem para atingir outro tipo de aplicações distribuídas, e também incluir o modelo matemático deste trabalho dentro de serviços na núvem que ofereçam plataformas MapReduce, a fim de ajudar os usuários a planejar suas implementações. / In the last years, Cloud Computing has become a key technology that made possible running applications without needing to deploy a physical infrastructure with the advantage of lowering costs to the user by charging only for the computational resources used by the application. The challenge with deploying distributed applications in Cloud Computing environments is that the virtual machine infrastructure should be planned in a way that is time and cost-effective. Also, in the last years we have seen how the amount of data produced by applications has grown bigger than ever. This data contains valuable information that has to be extracted using tools like MapReduce. MapReduce is an important framework to analyze large amounts of data since it was proposed by Google, and made open source by Apache with its Hadoop implementation. The goal of this work is to show that the execution time of a distributed application, namely, a MapReduce application, in a Cloud computing environment, can be predicted using a mathematical model based on theoretical specifications. This prediction is made to help the users of the Cloud Computing environment to plan their deployments, i.e., quantify the number of virtual machines and its characteristics in order to have a lesser cost and/or time. After measuring the application execution time and varying parameters stated in the mathematical model, and after that, using a linear regression technique, the goal is achieved finding a model of the execution time which was then applied to predict the execution time of MapReduce applications with satisfying results. The experiments were conducted in several configurations: namely, private and public clusters, as well as commercial cloud infrastructures, running different MapReduce applications, and varying the number of nodes composing the cluster, as well as the amount of workload given to the application. Experiments showed a clear relation with the theoretical model, revealing that the model is in fact able to predict the execution time of MapReduce applications. The developed model is generic, meaning that it uses theoretical abstractions for the computing capacity of the environment and the computing cost of the MapReduce application. Further work in extending this approach to fit other types of distributed applications is encouraged, as well as including this mathematical model into Cloud services offering MapReduce platforms, in order to aid users plan their deployments.

Diante do elevado nível de competição presente no cenário atual, torna-se indispensável a adoção de medidas de gestão capazes de priorizar e dirigir esforços na busca pela excelência no desempenho operacional das empresas. Assim, o objetivo deste trabalho é propor uma abordagem baseada em custos de produção e previsão de demanda voltada ao planejamento e controle da capacidade produtiva. Inicialmente, realizou-se o mapeamento dos artigos publicados em dezenove periódicos no período de 2002 a 2013, visando identificar abordagens relacionadas ao tema custos de produção e o processo de previsão de demanda. Com a finalidade de otimizar o planejamento da capacidade produtiva disponível, uma modelagem matemática, que utiliza o algoritmo generalized reduced gradient (GRG) não linear, é proposta. Por fim, é apresentado um modelo para o controle do desempenho operacional do sistema produtivo, fundamentado na avaliação de custos e no planejamento da capacidade de produção. Para avaliar a eficácia do modelo proposto, este foi aplicado em uma empresa de manufatura de materiais plásticos para a construção civil, em um sistema com múltiplos produtos e múltiplas máquinas. / Given the high level of competition present in the current environment, it is essential to adopt management measures in order to prioritize and direct efforts in the search for excellence in the company operational performance. Therefore, the objective of this study is to propose an approach based on production costs and demand forecasting focused on production capacity planning and control. A mapping of the articles published in nineteen journals, during the period of 2002 to 2013, was conducted to identify the different approaches related to production costs and demand forecasting. A mathematical modeling is proposed with the objective of optimizing the capacity planning using the nonlinear algorithm generalized reduced gradient (GRG). This study presented a model for controlling operational performance of the production system based on the evaluation of production costs and capacity planning. To evaluate the efficacy the model was applied to a manufacturing company of plastic materials for the construction, in a system with multi-products and multi-machines.

Orientadores: José Raimundo de Oliveira, Rafael Santos Mendes / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-20T15:05:32Z (GMT). No. of bitstreams: 1 FereciniNeto_Angelo_M.pdf: 2146836 bytes, checksum: 85b8ddc65061527b403e8635673bd89d (MD5) Previous issue date: 2012 / Resumo: O planejamento de capacidade de sistemas é um método muito útil para predizer qual hardware será necessário para processar um sistema de TI (Tecnologia da Informação) com alto desempenho. A ferramenta proposta foi desenvolvida para facilitar este difícil e importante trabalho, automatizando a análise do software em diferentes ambientes. Através da simulação discreta orientada a eventos, baseada em um modelo de teoria de filas, ela modela a interação entre hardware e softwares, simplificando a análise de qualquer sistema batch de TI. Neste tipo de sistema, normalmente existem muitas transações que necessitam ser processadas pelo sistema sem interação humana e frequentemente são utilizados os benefícios do processamento paralelo para um melhor desempenho. Podemos aplicar estas idéias para representar a carga de trabalho no modelo de simulação simplesmente como processos que entram no sistema para serem processados. Os resultados mostraram que é possível utilizar um modelo para representar o sistema real com uma precisão de até 30% / Abstract: System Capacity Planning is a very useful method to predict which hardware will be necessary to run a high performance IT system. The proposed tool was developed to ease this hard and important job, automating the analysis of the software in different environments. It uses the discrete-event simulation using a queue theory model to represent the hardware and software interaction. The model presented here represents a simplification of any batch IT systems. In this type of system, commonly we have a lot of transactions that need to be processed by the system without human interaction and frequently use the benefits of parallel processing for better performance. We can apply these ideas to represent the workload in the simulation model as simple tasks that enter the system to be processed. Results show that it is possible to use a model to represent the real system with 30% fidelity / Mestrado / Engenharia de Computação / Mestre em Engenharia Elétrica

Saab has a product, Remote Tower, which allows air traffic controllers to supervise and control an airport remotely. Today an air traffic controller can operate on one airport at the time and switch between airports. The next phase of this system is to allow the air traffic controller to simultaneously control multiple airports, i.e. going from controlling airports in sequence to controlling airports in parallel. The airports are connected to and controlled from one single airport control center. This leads to a planning problem, both in long term and short term, for staffing this airport control center to maintain the capacity of the connected airports. This thesis will present the process of the development of a tool allowing a user to plan the schedule the staffing of a remote tower center. By utilizing this tool the impact of different restrictions affecting the planning will be evaluated and presented in the results.

This thesis considers a common healthcare challenge of planning capacity for a system of care where patients receive multiple treatments sessions from multiple resources. As a case study of this more general problem, we considered the particular context of a capacity planning model for the Mood and Anxiety Program at The Ottawa Royal Health Centre (referenced as The Royal for simplicity) where a new service system known as CAPA (www.capa.co.uk) is being implemented to enhance the mental care provided to its patients. In order to develop the capacity planning model, we have created a simulation model using the Arena simulation software. We have also used the ABCMod Framework as the modeling methodology. The ABCMod is an activity based conceptual modelling framework that provides a set of guidelines as to how to build a conceptual model including its structural and behavioural aspects as well as a collection of constructs which include inputs, outputs and parameters among others. The ABCMod framework tools are expected to facilitate the model validation with project stakeholders. A series of scenarios relevant to The Royal were modeled and analyzed in order to determine how best to manage capacity so certain performance goals within the CAPA system implementation are met. These scenarios determine the service level The Royal can provide with its current capacity and also the amount and distribution of resources that is required to achieve its goals under the CAPA system. As a result of our simulation runs, we defined the policy implications for The Royal in order to achieve its targets and successfully implement CAPA. Additionally, through the application of the ABCMod framework and standard process mapping tools, we were able to reach a consensus and validate our modeling approach with the project stakeholders at The Royal. Our model could be adapted to other settings in which multiple resources provide a series of sequential interventions to clients.

During the last decade the gap between software modeling and performancemodeling has been closing. For example, UML annotations have been developed to enable the transformation of UML software models to performance models, thereby making performance modeling more accessible. However, as of yet few of these tools are ready for industrial application. In this thesis we explorer the current state of performance modeling tooling, the selection of a performance modeling tool for industrial application is described and a performance modeling case study on one of ABB's remote diagnostics systems (RDS) is presented. The case study shows the search for the best architectural alternative during a multi-million dollar redesign project of the ASP.Net web services based RDS back-end. The performance model is integrated with a cost model to provide valuable decision support for the construction of an architectural roadmap. Despite our success we suggest that the stability of software performance modeling tooling and the semantic gap between performance modeling and software architecture concepts are major hurdles to widespread industrial adaptation. Future work may use the experiences recorded in this thesis to continue improvement of performance modeling processes and tools for industrial use.

This diploma thesis deals with a topic of project management. The first part is focused on a research behind the theory of project management. In the second part, an analysis of the current state of project management in a selected company producing machine tools is performed first. Then it is followed by a proposal to change the process. In the last part, the individual proposals are then analysed and shown on a real project. These proposals could lead to improvements in productivity and product after implementation.

This thesis aims to clarify the use of Excel for capacity planning, how an excel-based capacityplanning tool works, and to present a way to implement a web system suitable for ad-hoc capacity and release planning. Capacity design is a critical part of flexible software development teams. Excel spreadsheets are flexible but not ideal for large organizations where many users have access to and can change planning concurrently. The existing tool received low ratings in terms of helpfulness, robustness to the inappropriate user, universality, flexibility, and aesthetics but performed quite well in clarity, consistency, memorability, overall efficiency, and third-party safety. The tool had some additional benefits, including calculating team capacity, preventing overplanning of individual development teams, and the ability to register the outcome of past planning periods. We implemented a web system based on Blazor Web to support the tasks provided by the existing tool, including capacity planning and release planning. The web system also supported role-based authorization. This web system was evaluated in a usability survey and maintained most of the provided functionality of the existing tool while avoiding some of the tool’s usability-related problems.

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