An Extended TOPSIS Approach for Ranking Cloud Service Providers

Rădulescu, Constanţa Zoie; Radulescu, Iulia Cristina

doi:10.24846/v26i2y201706

Cited by 39 publications

(15 citation statements)

References 6 publications

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“…To evaluate the performance of players using this group of monitored indicators, the TOPSIS technique was selected, the application of which has been proven in numerous fields, including tourism [90], transportation [91], agriculture [54], risk assessment [92], the evaluation of cloud services providers [93] and the evaluation of local government entities [50]. One of the most important steps in every MCDM method, including TOPSIS, is weighting, or determining the importance of the evaluated indicators.…”

Section: Discussionmentioning

confidence: 99%

An Analysis of Usage of a Multi-Criteria Approach in an Athlete Evaluation: An Evidence of NHL Attackers

Vavrek

2021

Mathematics

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The presented research focuses on the commonly used Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), which is applied to an evaluation of a basic set of 581 national hockey league (NHL) players in the 2018/2019 season. This is used in combination with a number of objective methods for weighting indicators for identifying differences in their usage. A total of 11 indicators with their own testimonial values, including points, hits, blocked shots and more, are selected for this purpose. The selection of a method for weighting indicators has a major influence on the results obtained and the differences between them, and maintains the internal links within the ranked set of players. Of the evaluated methods, we prefer the Mean Weight method, and we recommend that the input indicators be considered equivalent when evaluating athletes.

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Section: Discussionmentioning

confidence: 99%

An Analysis of Usage of a Multi-Criteria Approach in an Athlete Evaluation: An Evidence of NHL Attackers

Vavrek

2021

Mathematics

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“…I. [24] by modifying the variable ρ in the Minkowski distance measure proposed an extended TOPSIS method for ranking cloud service providers. Verma et al [30] proposed an interval-valued intuitionistic fuzzy TOPSIS method for solving a facility location problem.…”

Section: Section Headmentioning

confidence: 99%

An extension of the TOPSIS for multi-attribute group decision making under neutrosophic environment

Nafei¹,

Gu²,

Yuan³

2021

MMN

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Neutrosophic set theory as a generalization of the fuzzy set theory and intuitionistic fuzzy set theory is an effective tool to deal with inconsistent, imprecise, and vague information. TOPSIS is a multiple attribute method to identify solutions from a finite set of alternatives based upon simultaneous minimization of distance from an ideal point and maximization of distance from a nadir point. In this paper, we first develop a new Hamming distance between single-valued neutrosophic numbers and then present an extension of the TOPSIS method for multi-attribute group decision-making (MAGDM) based on single-valued neutrosophic sets, where the inform ation about attribute values and attribute weights are expressed by decision-makers based on neutrosophic numbers.

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“…This is a multi-criteria decision making (MCDM) problem. MCDM methods AHP [1] [8], TOPSIS [11], VIKOR [12], and ELECTRE [13]are widely used in cloud environment.…”

Section: Introductionmentioning

confidence: 99%

A Ranking Based Model for Selecting Optimum Cloud Geographical Region

Neeraj¹,

Singh²,

Singh³

2019

IJITEE

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Cloud computing has become a dominant service computing model where the services such as software, platform, infrastructure are provided to the cloud consumers on demand basis using pay as you go model. Every cloud consumer requires high performance service in minimal cost. The performance of service in cloudis measured by the parameters availability, agility, cost, and security etc. The service performance and cost are very much dependent on the cloud geographical region (CGR) where these are deployed. The services offered by the cloud service provider are installed on multiple data centers located at different CGR. In cloud environment, the selection of a service installed on the optimum CGR within the limited time overhead is a challenging and interesting problem. In this paper, the multi criteria decision making method, PROMETHEE II and objective weighting method Shannon’s Entropy,based ranking model is proposed for solving the optimum CGR selection problem. The CGR dataset of Amazon Web Service is used for the numerical analysis. The sensitivity analysis is performed for validating the stability of the proposed model and getting the most sensitive parameter. The applicability and usefulness of the service selection process is validated through the experimental results on synthetic dataset. Results show that the service selection process is achieved withlimited time overhead and hence suitable for online selection process in cloud.

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An Extended TOPSIS Approach for Ranking Cloud Service Providers

Cited by 39 publications

References 6 publications

An Analysis of Usage of a Multi-Criteria Approach in an Athlete Evaluation: An Evidence of NHL Attackers

An Analysis of Usage of a Multi-Criteria Approach in an Athlete Evaluation: An Evidence of NHL Attackers

An extension of the TOPSIS for multi-attribute group decision making under neutrosophic environment

A Ranking Based Model for Selecting Optimum Cloud Geographical Region

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