A user centric service-oriented modeling approach

Cheng, Ding-Yuan; Chao, Kuo‐Ming; Lo, Chi-Chun; Tsai, Chen‐Fang

doi:10.1007/s11280-011-0115-7

Cited by 36 publications

(28 citation statements)

References 63 publications

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“…The paper [1] describes a user centric serviceoriented modeling approach which is featured by integrating fuzzy TOPSIS method and Service Component Architecture (SCA) to facilitate web service selection and composition. It effectively satisfies a group of service consumers' subjective requirements and preferences in a dynamic environment.…”

Section: One Of the Most Used Madm Methods Is Technique For Order Prementioning

confidence: 99%

An Extended TOPSIS Approach for Ranking Cloud Service Providers

Rădulescu¹,

Radulescu²

2017

SIC

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There are many Cloud Service Providers (CSPs) in the global cloud computing market and customers may need to use scientific decision making methods for evaluating and ranking the CSPs according to their own requirements. Among the several approaches that have been proposed to solve the CSPs evaluation and ranking problem, there are Multi Attribute Decision Making (MADM) methods. One of the most commonly used MADM method is Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). In this paper, we extend the TOPSIS method by using the Minkowski distance. We propose an Extended TOPSIS (E-TOPSIS) approach by varying the parameter p in the Minkowski distance. The applicability of the proposed E-TOPSIS approach is presented in a case study for CSPs evaluation and ranking in relation to a set of Service Measurement Index -SMI criteria. An analysis of E-TOPSIS solutions and the CSPs order change relative to parameter p variation is realized. A comparison of the E-TOPSIS solutions with TOPSIS solution is presented.

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Section: One Of the Most Used Madm Methods Is Technique For Order Prementioning

confidence: 99%

An Extended TOPSIS Approach for Ranking Cloud Service Providers

Rădulescu¹,

Radulescu²

2017

SIC

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“…Out of these twenty two models, six [11][12][13][14][15][16] were using data mining techniques and the remaining sixteen [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32] were using MCDA. Moreover, out of sixteen MCDA based models, nine [22][23][24][25][26][27][28][29][30] were used to perform ranking and the rest [17-21, 31, 32] were used to optimize resources in the cloud. The literature review of nine MCDA based models used by online broker to generate ranking is presented below.…”

Section: Lierature Review and Research Gapmentioning

confidence: 99%

“…TrAdeCIS is the first methodology that supports an automated and quantified trade-offs based decision making for selection of a best cloud based service. In [29] authors propose a model which uses Fuzzy TOPSIS for web service selection. Based on the fact that web service selection is highly influenced by customer preferences, a simulated environment represented by 8x8 LED matrices on a circuit board was used to demonstrate the selection.…”

Section: Lierature Review and Research Gapmentioning

confidence: 99%

Cloud Service Providers Optimized Ranking Algorithm Based on Machine Learning and Multi-Criteria Decision Analysis

Mu¹,

Aaza²,

Bouvry³

et al. 2018

Preprint

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Abstract-Multi-criteria decision analysis (MCDA), one of the prevalent branches of operations research, aims to design mathematical and computational tools for selecting the best alternative among several choices with respect to specific criteria. In the cloud, MCDA based online brokers uses customer specified criteria to rank different service providers. However, subjected to limited domain knowledge, the customer may exclude relevant or include irrelevant criterion, which could result in suboptimal ranking of service providers. To deal with such misspecification, this research proposes a model, which uses notion of factor analysis from the domain of unsupervised machine learning. The model is evaluated using two quality-of-service (QoS) based datasets. The first dataset i.e., feedback from customers, was compiled using leading review websites such as Cloud Hosting Reviews, Best Cloud Computing Providers, and Cloud Storage Reviews and Ratings. The second dataset i.e., feedback from servers, was generated from cloud brokerage architecture that was emulated using high performance computing (HPC) cluster at University of Luxembourg (HPC @ Uni.lu). The simulation runs in a stable cloud environment i.e. when uncertainty is low, shows that online broker (equipped with the proposed model) produces optimized ranking of service providers as compared to other brokers. This is due the fact that proposed model assigns priorities to criteria objectively (using machine learning) rather than using priorities based on subjective judgments of the customer. This research will benefit potential cloud customers that view insufficient domain knowledge as a limiting factor for acquisition of web services in the cloud.Keywords-multi-criteria decision analysis (MCDA), online broker, misspecification of criteria, structural uncertainty, unsupervised machine learning, factor analysis, quality of service (QoS).

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“…One of the major differences between Fuzzy-TOPSIS and Classical-TOPSIS is that the later utilizes precisely known ratings and weights of the criteria [21]. Fuzzy-TOPSIS is an application of Fuzzy logic and FS.…”

Section: Fuzzy Topsis Algorithmmentioning

confidence: 99%

Choose Best Criteria for Decision Making Via Fuzzy Topsis Method

Zulqarnain¹

2017

MCS

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Abstract:Multi Criteria Decision Making (MCDM) uses different techniques to find a best alternative from multialternative and multi-criteria conditions. TOPSIS is an important practical technique for ranking and selection of different alternatives by using distance measures. Classical TOPSIS uses crisp techniques for the linguistic assessments, but due to imprecise and fuzziness nature of the linguistic assessments, we faced some problems to find out the solution of these problems. We proposed a Fuzzy TOPSIS with its example in this work and use this method for decision making.

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A user centric service-oriented modeling approach

Cited by 36 publications

References 63 publications

An Extended TOPSIS Approach for Ranking Cloud Service Providers

An Extended TOPSIS Approach for Ranking Cloud Service Providers

Cloud Service Providers Optimized Ranking Algorithm Based on Machine Learning and Multi-Criteria Decision Analysis

Choose Best Criteria for Decision Making Via Fuzzy Topsis Method

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