A Cost-Effective Intelligent Configuration Model in Cloud Computing

Tsai, Wei‐Tek; Qi, Guanqiu; Chen, Yinong

doi:10.1109/icdcsw.2012.46

Cited by 22 publications

(16 citation statements)

References 11 publications

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“…Trummer et al [17] interpret the outsourcing of part of the IT-stack as a constraint optimisation problem, that they solve using existing solvers. Tsai et al [18] consider a similar approach, choosing between different cloud providers using data mining and trend analysis techniques, and looking for minimising cost. In a related research, Sundareswaran et al [16] propose indexing and ranking cloud providers using a set of algorithms based on user preferences.…”

Section: Cloud Migrationmentioning

confidence: 98%

“…It is necessary to model and structure it to enable ease of use and understanding of the services by an end-user, and to enable automated support. Most of the academic works that have approached migration issues [6,7,[14][15][16][17][18] deal with reduced subsets of the providers' configuration spaces. However, when we plan for cloud adoption (or migration), we have to address the whole configuration space to find the most suitable configuration.…”

Section: Problemmentioning

confidence: 99%

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Automated configuration support for infrastructure migration to the cloud

García-Galán

Trinidad

Rana

et al. 2016

Future Generation Computer Systems

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h i g h l i g h t s• We support the decision making in migration planning to the cloud.• We use Feature Models to describe the configuration space of an IaaS. • We automate the search of the most suitable IaaS configuration. • Our approach improves the results of commercial applications on Amazon EC2. a b s t r a c tWith an increasing number of cloud computing offerings in the market, migrating an existing computational infrastructure to the cloud requires comparison of different offers in order to find the most suitable configuration. Cloud providers offer many configuration options, such as location, purchasing mode, redundancy, and extra storage. Often, the information about such options is not well organised. This leads to large and unstructured configuration spaces, and turns the comparison into a tedious, errorprone search problem for the customers. In this work we focus on supporting customer decision making for selecting the most suitable cloud configuration-in terms of infrastructural requirements and cost. We achieve this by means of variability modelling and analysis techniques. Firstly, we structure the configuration space of an IaaS using feature models, usually employed for the modelling of variabilityintensive systems, and present the case study of the Amazon EC2. Secondly, we assist the configuration search process. Feature models enable the use of different analysis operations that, among others, automate the search of optimal configurations. Results of our analysis show how our approach, with a negligible analysis time, outperforms commercial approaches in terms of expressiveness and accuracy.

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Section: Cloud Migrationmentioning

confidence: 98%

Section: Problemmentioning

confidence: 99%

Automated configuration support for infrastructure migration to the cloud

García-Galán

Trinidad

Rana

et al. 2016

Future Generation Computer Systems

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“…When the Gaussian filter algorithm is implemented as a low-pass filter to smooth the input source image, low-frequency component I l of the input source image can be got. Then high-frequency component can be got by Equation (2).…”

Section: Gaussian-filter Based Decompositionmentioning

confidence: 99%

“…Image fusion techniques are used to combine multiple images from the same senor modality or different sensor modalities for enhancing visibility. Following the development of cloud computing, cloud environment provides more and more strong computation capacity to process various images [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

An Integrated Dictionary-Learning Entropy-Based Medical Image Fusion Framework

Wang

Zhang

et al. 2017

Future Internet

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Abstract:Image fusion is widely used in different areas and can integrate complementary and relevant information of source images captured by multiple sensors into a unitary synthetic image. Medical image fusion, as an important image fusion application, can extract the details of multiple images from different imaging modalities and combine them into an image that contains complete and non-redundant information for increasing the accuracy of medical diagnosis and assessment. The quality of the fused image directly affects medical diagnosis and assessment. However, existing solutions have some drawbacks in contrast, sharpness, brightness, blur and details. This paper proposes an integrated dictionary-learning and entropy-based medical image-fusion framework that consists of three steps. First, the input image information is decomposed into low-frequency and high-frequency components by using a Gaussian filter. Second, low-frequency components are fused by weighted average algorithm and high-frequency components are fused by the dictionary-learning based algorithm. In the dictionary-learning process of high-frequency components, an entropy-based algorithm is used for informative blocks selection. Third, the fused low-frequency and high-frequency components are combined to obtain the final fusion results. The results and analyses of comparative experiments demonstrate that the proposed medical image fusion framework has better performance than existing solutions.

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“…(Clark et al, 2012) propose an Intelligent Cloud Resource Allocation Service to evaluate the most suitable configuration given consumer preferences. (Tsai et al, 2012) considers a similar approach, choosing between different Cloud providers using data mining and trend analysis techniques, and looking for minimising cost. In a related research, (Sundareswaran et al, 2012) propose indexing and ranking Cloud providers using a set of algorithms based on user preferences.…”

Section: Related Workmentioning

confidence: 99%

Migrating to the Cloud - A Software Product Line based Analysis

García-Galán

Rana

Trinidad

et al. 2013

Proceedings of the 3rd International Conference on Cloud Computing and Services Science

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Abstract:Identifying which part of a local system should be migrated to a public Cloud environment is often a difficult and error prone process. With the significant (and increasing) number of commercial Cloud providers, choosing a provider whose capability best meets requirements is also often difficult. Most Cloud service providers offer large amounts of configurable resources, which can be combined in a number of different ways. In the case of small and medium companies, finding a suitable configuration with the minimum cost is often an essential requirement to migrate, or even to initiate the decision process for migration. We interpret this need as a problem associated with variability management and analysis. Variability techniques and models deal with large configuration spaces, and have been proposed previously to support configuration processes in industrial cases. Furthermore, this is a mature field which has a large catalog of analysis operations to extract valuable information in an automated way. Some of these operations can be used and tailored for Cloud environments. We focus in this work on Amazon Cloud services, primarily due to the large number of possible configurations available by this service provider and its popularity. Our approach can also be adapted to other providers offering similar capabilities.

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A Cost-Effective Intelligent Configuration Model in Cloud Computing

Cited by 22 publications

References 11 publications

Automated configuration support for infrastructure migration to the cloud

Automated configuration support for infrastructure migration to the cloud

An Integrated Dictionary-Learning Entropy-Based Medical Image Fusion Framework

Migrating to the Cloud - A Software Product Line based Analysis

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