CCS-OSSR: A framework based on Hybrid MCDM for Optimal Service Selection and Ranking of Cloud Computing Services

Kumar, Rakesh Ranjan; Kumari, Binita; Kumar, Chiranjeev

doi:10.1007/s10586-020-03166-3

Cited by 36 publications

(18 citation statements)

References 45 publications

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“…However, there are usually conflicts between these diverse criteria. The MCDM methodology can be used to directly evaluate conflicting or nonconflicting appraised criteria to not only detect and interactively measure the optimal and suboptimal orders of each solution (25,26) but also consider and measure the characteristics and attitudes of the conflicting or nonconflicting appraised criteria.…”

Section: Research On Measurement Models and Methodsmentioning

confidence: 99%

Use of Sensor Technologies in Online Courses in Post-COVID-19 Era

Wu¹,

Hsieh²,

Min³

et al. 2021

Sensors and Materials

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In this study, educational social learning theory and a statistical multiple-criteria decisionmaking (MCDM) methodology are creatively cross-employed to comprehensively cross-evaluate online courses and sensor technologies. This was accomplished by means of an in-depth survey of large-scale current online-course users and professional experts with the highest research reliability, validity, accuracy, and representativeness. The three most valuable and contributive conclusions of this study are as follows: (1) The repurposing technology function (RTF) of online-course technology can combine software sensor (SS), motion sensor (MS), and environment sensor (ES) technologies to not only detect moving objects but also achieve cognition in the environment (e.g., by using a face sensor) to extract emotions of course participants in response to words and phrases during lectures to increase online-course learning performance. (2) The course professionalization technology function (CPTF) of online-course technology can merge SS, MS, and ES technologies to control online-course hardware sensor devices and equipment to control the depth and span of online-course content to strengthen online-course learning performance. (3) The course evaluation technology function (CETF) of online-course technology can consolidate SS, MS, and ES technologies to not only empirically evaluate online-course implementation but also indirectly appraise online-course learning performance.

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Section: Research On Measurement Models and Methodsmentioning

confidence: 99%

Use of Sensor Technologies in Online Courses in Post-COVID-19 Era

Wu¹,

Hsieh²,

Min³

et al. 2021

Sensors and Materials

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show abstract

“…Eleven publications about MCDM methods proved to have serious deficiencies [36] including high computational complexity (and hence high time demand of calculations), inconsistency and problems of pairwise comparisons. Kumar et al defined twelve quality of criteria and weights were calculated by TOPSIS (a best worst method) They claim a framework for the application of one MCDM method to "trustworthy" cloud service selection without PCA or any variants of factor analysis.…”

Section: Introductionmentioning

confidence: 99%

Factor analysis, sparse PCA, and Sum of Ranking Differences-based improvements of the Promethee-GAIA multicriteria decision support technique

et al. 2022

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The Promethee-GAIA method is a multicriteria decision support technique that defines the aggregated ranks of multiple criteria and visualizes them based on Principal Component Analysis (PCA). In the case of numerous criteria, the PCA biplot-based visualization do not perceive how a criterion influences the decision problem. The central question is how the Promethee-GAIA-based decision-making process can be improved to gain more interpretable results that reveal more characteristic inner relationships between the criteria. To improve the Promethee-GAIA method, we suggest three techniques that eliminate redundant criteria as well as clearly outline, which criterion belongs to which factor and explore the similarities between criteria. These methods are the following: A) Principal factoring with rotation and communality analysis (P-PFA), B) the integration of Sparse PCA into the Promethee II method (P-sPCA), and C) the Sum of Ranking Differences method (P-SRD). The suggested methods are presented through an I4.0+ dataset that measures the Industry 4.0 readiness of NUTS 2-classified regions. The proposed methods are useful tools for handling multicriteria ranking problems, if the number of criteria is numerous.

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“…Therefore, selecting an optimum service out of many possibilities is a challenging task for cloud users. It is better to remember that these cloud services considerably differ regarding some quality of service (QoS) quantifiable criteria, such as performance and cost (Kumar et al , 2020). Finally, this difficulty has encouraged some research in cloud service selection in cloud computing, in which various mathematical methods have been utilized depending on various criteria.…”

Section: Introductionmentioning

confidence: 99%

Toward the efficient service selection approaches in cloud computing

Rahimi

Navimipour

Hosseinzadeh

et al. 2021

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PurposeThis paper follows a systematic literature review (SLR) method covering the published studies until March 2021. The authors have extracted the related studies from different online databases utilizing quality-assessment-criteria. In order to review high-quality studies, 32 papers have been chosen through the paper selection process. The selected papers have been categorized into three main groups, decision-making methods (17 papers), meta-heuristic methods (8 papers) and fuzzy-based methods (7 papers). The existing methods in each group have been examined based on important qualitative parameters, namely, time, cost, scalability, efficiency, availability and reliability.Design/methodology/approachCloud computing is known as one of the superior technologies to perform large-scale and complex computing. With the growing tendency of network service users to utilize cloud computing, web service providers are encouraged to provide services with various functional and non-functional features and supply them in a service pool. In this regard, choosing the most appropriate services to fulfill users' requirements becomes a challenging problem. Since the problem of service selection in a cloud environment is known as a nondeterministic polynomial time (NP)-hard problem, many efforts have been made in recent years. Therefore, this paper aims to study and assess the existing service selection approaches in cloud computing.FindingsThe obtained results indicate that in decision-making methods, the assignment of proper weights to the criteria has a high impact on service ranking accuracy. Also, since service selection in cloud computing is known as an NP-hard problem, utilizing meta-heuristic algorithms to solve this problem offers interesting advantages compared to other approaches in discovering better solutions with less computational effort and moving quickly toward very good solutions. On the other hand, since fuzzy-based service selection approaches offer search results visually and cover quality of service (QoS) requirements of users, this kind of method is able to facilitate enhanced user experience.Research limitations/implicationsAlthough the current paper aimed to provide a comprehensive study, there were some limitations. Since the authors have applied some filters to select the studies, some effective works may have been ignored. Generally, this paper has focused on journal papers and some effective works published in conferences. Moreover, the works published in non-English formats have been excluded. To discover relevant studies, the authors have chosen Google Scholar as a popular electronic database. Although Google Scholar can offer the most valid approaches, some suitable papers may not be observed during the process of article selection.Practical implicationsThe outcome of the current paper will be useful and valuable for scholars, and it can be a roadmap to help future researchers enrich and improve their innovations. By assessing the recent efforts in service selection in cloud computing and offering an up-to-date comparison of the discussed works, this paper can be a solid foundation for understanding the different aspects of service selection.Originality/valueAlthough service selection approaches have essential impacts on cloud computing, there is still a lack of a detailed and comprehensive study about reviewing and assessing existing mechanisms in this field. Therefore, the current paper adopts a systematic method to cover this gap. The obtained results in this paper can help the researchers interested in the field of service selection. Generally, the authors have aimed to specify existing challenges, characterize the efficient efforts and suggest some directions for upcoming studies.

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CCS-OSSR: A framework based on Hybrid MCDM for Optimal Service Selection and Ranking of Cloud Computing Services

Cited by 36 publications

References 45 publications

Use of Sensor Technologies in Online Courses in Post-COVID-19 Era

Use of Sensor Technologies in Online Courses in Post-COVID-19 Era

Factor analysis, sparse PCA, and Sum of Ranking Differences-based improvements of the Promethee-GAIA multicriteria decision support technique

Toward the efficient service selection approaches in cloud computing

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