Multi-criteria intuitionistic fuzzy group decision analysis with TOPSIS method for selecting appropriate cloud solution to manage big data projects

Sachdeva, Nitin; Singh, Ompal; Kapur, P. K.; Galar, Diego

doi:10.1007/s13198-016-0455-x

Cited by 14 publications

(8 citation statements)

References 28 publications

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“…In [24] extent analysis is utilised to calculate the FTOPSIS with affinity weight. This example is used to rank among five recognize cloud solutions for big data projects.…”

Section: Resultsmentioning

confidence: 99%

“…Based on these three experts, it was found that Google is the best cloud platform compared to Amazon, HP Cloud, Rackspace and Microsoft Azure. Besides, this paper also provided a comparative analysis between the proposed method with two previous methods (FTOPSIS by [14] and Intuitionistic with TOPSIS by [24]). Finally, we can conclude that FTOPSIS with affinity weight is a practical and compatible method to solve big data projects problem.…”

Section: Discussionmentioning

confidence: 99%

“…In order to validate this proposed method, we provide a comparative analysis study. Two different comparative analysis using the previous method which are FTOPSIS [14] and Intuitionistic with TOPSIS [24] were used to validate the proposed method. Thus, full results are summarized as Table 11.…”

Section: G Step 7: the Final Calculations Of Relative Closenessmentioning

confidence: 99%

“…In [23] integrated procurement management of big data analyst using fuzzy VIKOR technique. In [24] selected an appropriate cloud solution problem using the combination of FTOPSIS with an intuitionistic fuzzy set method. However, the previous studies neglected the performance, schedule, traffic management in a network of cloud solutions problems.…”

Section: Introductionmentioning

confidence: 99%

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A Fuzzy TOPSIS with Affinity Weight for Big Data Projects: Managing Cloud Solution Problems

2019

ijrte

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This study underlines a fuzzy decision making method for determining weights of criteria that constitute for solving cloud solution problems in managing big data projects. The weight determination of cloud for big data projects is crucial since many uncertain and vagueness criteria that need to be considered concurrently. Furthermore, these criteria involve network performance, schedule and traffic management of cloud solutions problem. In response to these challenges, the affinity set applies to Fuzzy TOPSIS (FTOPSIS) method to propose timedependent weights of three criteria for managing big data projects. A major advantage of the affinity weights is that it incorporates performance-traffic management relationships between all criteria. This affinity weight with FTOPSIS method helps to solve the cloud solution problems. This paper also includes the same examples with different methods to compare and validate the proposed method. The proposed seven-step of affinity weight with FTOPSIS method finally managed to solve the cloud solution problem and the result was beautifully consistent with the other two methods.

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“…In [24] extent analysis is utilised to calculate the FTOPSIS with affinity weight. This example is used to rank among five recognize cloud solutions for big data projects.…”

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: G Step 7: the Final Calculations Of Relative Closenessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Fuzzy TOPSIS with Affinity Weight for Big Data Projects: Managing Cloud Solution Problems

2019

ijrte

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“…Lee et al [22] proposed a hybrid multi-criteria decision-making model for a cloud service selection problem using balanced scorecard (BSC), fuzzy Delphi method (FDM) and fuzzy analytical hierarchy process (FAHP). Sachdeva et al [36] combined a hybrid TOPSIS method with an intuitionistic fuzzy set to select appropriate cloud solutions to manage big data projects in a group decision-making environment. Garg et al [14] proposed a framework called SMICloud, which compares different cloud providers based on user requirements.…”

Section: Related Workmentioning

confidence: 99%

Decision making in cloud environments: an approach based on multiple-criteria decision analysis and stochastic models

et al. 2018

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Cloud computing is a paradigm that provides services through the Internet. The paradigm has been influenced by previously available technologies (for example cluster, peer-to-peer, and grid computing) and has now been adopted by almost all large organizations. Companies such as Google, Amazon, Microsoft and Facebook have made significant investments in cloud computing, and now provide services with high levels of dependability. The efficient and accurate assessment of cloud-based infrastructure is fundamental in guaranteeing both business continuity and uninterrupted public services, as much as is possible. This paper presents an approach for selecting cloud computing infrastructures, in terms of dependability and cost that best suits both company and customer needs. We use stochastic models to calculate dependability-related metrics for different cloud infrastructures. We then use a Multiple-Criteria Decision-Making (MCDM) method to rank the best cloud infrastructures, taking customer service constraints such as reliability, downtime, and cost into consideration. A case study demonstrates the practicability and usefulness of the proposed approach.

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A Brief Review on Multi-Attribute Decision Making in the Emerging Fields of Computer Science

Nath

Das

Debnath

2022

Communications in Computer and Information Science

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Multi-criteria intuitionistic fuzzy group decision analysis with TOPSIS method for selecting appropriate cloud solution to manage big data projects

Cited by 14 publications

References 28 publications

A Fuzzy TOPSIS with Affinity Weight for Big Data Projects: Managing Cloud Solution Problems

A Fuzzy TOPSIS with Affinity Weight for Big Data Projects: Managing Cloud Solution Problems

Decision making in cloud environments: an approach based on multiple-criteria decision analysis and stochastic models

A Brief Review on Multi-Attribute Decision Making in the Emerging Fields of Computer Science

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