Design and implementation of a cloud computing adoption decision tool: Generating a cloud road

Migrating to cloud computing is one of the current enterprise challenges. This technology provides a new paradigm based on "on-demand payment" for information and communication technologies. In this sense, the small and medium enterprise is supposed to be the most interested, since initial investments are avoided and the technology allows gradual implementation. However, even if the characteristics and capacities have been widely discussed, entry into the cloud is still lacking in terms of practical, real frameworks. This paper aims at filling this gap, presenting a real tool already implemented and tested, which can be used as a cloud computing adoption decision tool. This tool uses diagnosis based on specific questions to gather the required information and subsequently provide the user with valuable information to deploy the business within the cloud, specifically in the form of Software as a Service (SaaS) solutions. This information allows the decision makers to generate their particular Cloud Road. A pilot study has been carried out with enterprises at a local level with a two-fold objective: To ascertain the degree of knowledge on cloud computing and to identify the most interesting business areas and their related tools for this technology. As expected, the results show high interest and low knowledge on this subject and the tool presented aims to readdress this mismatch, insofar as possible. Copyright: © 2015 Bildosola et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Sistema de suporte à decisão espacial aplicado à análise da vulnerabilidade dos recursos hídricos na bacia Guapi-Macacu / RJ.

No contexto do planejamento e gestão dos recursos hídricos em bacias hidrográficas, é crescente a demanda por informações consistentes relativas ao estado do ambiente e pressões ambientais de forma integrada, para que possam informar à população e subsidiar atividades do setor público e privado. Essa demanda pode ser satisfeita com a modelagem e integração em um Sistema de Informações Geográficas (SIG), com propriedades e funções de processamento que permitem sua utilização em ambiente integrado. Desta forma, neste trabalho é apresentada uma metodologia para a avaliação muticriterial dos recursos hídricos de bacias hidrográficas, que vai desde a seleção de indicadores e definição dos pesos, até a execução de avaliações e espacialização de resultados. Esta metodologia é composta por duas fases: avaliação da vulnerabilidade dos recursos hídricos de uma bacia hidrográfica a partir do uso de sistemas de suporte à decisão espacial, e, avaliação da qualidade das águas através da adaptação de um Índice de Qualidade das Águas. Foi adotada uma base de conhecimento, sistemas de suporte à decisão, SIG e uma ferramenta computacional que integra estes resultados permitindo a geração de análises com cenários da vulnerabilidade dos recursos hídricos. Em paralelo, a qualidade das águas das sub-bacias hidrográficas foi obtida a partir da adaptação do cálculo do Índice de Qualidade das águas proposto pela Companhia de Tecnologia de Saneamento Ambiental (CETESB) e aplicação do Índice de Toxidez. Os resultados mostraram sub-bacias com seus recursos hídricos mais ou menos vulneráveis, bem como sub-bacias com toxidez acima da legislação. A avaliação integrada entre áreas mais vulneráveis e que apresentam menor qualidade e/ou maior toxidez poderá nortear a tomada de decisão e projetos visando a conservação dos recursos hídricos em bacias hidrográficas.

Data-based robust multiobjective optimization of interconnected processes: energy efficiency case study in papermaking.

Reducing energy consumption is a major challenge for "energy-intensive" industries such as papermaking. A commercially viable energy saving solution is to employ data-based optimization techniques to obtain a set of "optimized" operational settings that satisfy certain performance indices. The difficulties of this are: 1) the problems of this type are inherently multicriteria in the sense that improving one performance index might result in compromising the other important measures; 2) practical systems often exhibit unknown complex dynamics and several interconnections which make the modeling task difficult; and 3) as the models are acquired from the existing historical data, they are valid only locally and extrapolations incorporate risk of increasing process variability. To overcome these difficulties, this paper presents a new decision support system for robust multiobjective optimization of interconnected processes. The plant is first divided into serially connected units to model the process, product quality, energy consumption, and corresponding uncertainty measures. Then multiobjective gradient descent algorithm is used to solve the problem in line with user's preference information. Finally, the optimization results are visualized for analysis and decision making. In practice, if further iterations of the optimization algorithm are considered, validity of the local models must be checked prior to proceeding to further iterations. The method is implemented by a MATLAB-based interactive tool DataExplorer supporting a range of data analysis, modeling, and multiobjective optimization techniques. The proposed approach was tested in two U.K.-based commercial paper mills where the aim was reducing steam consumption and increasing productivity while maintaining the product quality by optimization of vacuum pressures in forming and press sections. The experimental results demonstrate the effectiveness of the method.

Data-based robust multiobjective optimization of interconnected processes: Energy efficiency case study in papermaking

Reducing energy consumption is a major challenge for energy-intensive industries such as papermaking. A commercially viable energy saving solution is to employ data-based optimization techniques to obtain a set of optimized operational settings that satisfy certain performance indices. The difficulties of this are: 1) the problems of this type are inherently multicriteria in the sense that improving one performance index might result in compromising the other important measures; 2) practical systems often exhibit unknown complex dynamics and several interconnections which make the modeling task difficult; and 3) as the models are acquired from the existing historical data, they are valid only locally and extrapolations incorporate risk of increasing process variability. To overcome these difficulties, this paper presents a new decision support system for robust multiobjective optimization of interconnected processes. The plant is first divided into serially connected units to model the process, product quality, energy consumption, and corresponding uncertainty measures. Then multiobjective gradient descent algorithm is used to solve the problem in line with user's preference information. Finally, the optimization results are visualized for analysis and decision making. In practice, if further iterations of the optimization algorithm are considered, validity of the local models must be checked prior to proceeding to further iterations. The method is implemented by a MATLAB-based interactive tool DataExplorer supporting a range of data analysis, modeling, and multiobjective optimization techniques. The proposed approach was tested in two U.K.-based commercial paper mills where the aim was reducing steam consumption and increasing productivity while maintaining the product quality by optimization of vacuum pressures in forming and press sections. The experimental results demonstrate the effectiveness of the method. © 2006 IEEE.