Aggregating Fuzzy Sentiments with Customized QoS Parameters for Cloud Provider Selection Using Fuzzy Best Worst and Fuzzy TOPSIS
Book chapter
Hussain, Walayat, Merigó, Jose, Rabhi, Fethi A. and Gao, Honghao. (2022). Aggregating Fuzzy Sentiments with Customized QoS Parameters for Cloud Provider Selection Using Fuzzy Best Worst and Fuzzy TOPSIS. In Soft Computing and Fuzzy Methodologies in Innovation Management and Sustainability pp. 81-92 Springer. https://doi.org/10.1007/978-3-030-96150-3_6
Authors | Hussain, Walayat, Merigó, Jose, Rabhi, Fethi A. and Gao, Honghao |
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Abstract | Consumers often get confused to select the best cloud providers from the huge marketplace. The hesitancy of consumers further escalates when multiple service providers offer the same type and quality of services. To deal with such an uncertainty, the decision-makers always combine multiple factors to make an informed choice. Sentiment mining is one of the key parameters to determine the service quality and get an insight into the business. It assists service providers in precisely deduce consumer's emotions regarding the product. The analysis helps providers fine-tune the product based on consumer's sentiment and accommodates the consumer's request to find an optimal service provider. Several existing literature for cloud service selection mainly focuses on the Quality of Service (QoS) of the offered services. However, very few of them have considered the user experience of a consumer in the decision-making process. Moreover, there is minimal literature that amalgamates Quality of Experience (QoE) with customized Quality of Service (QoS) requirements to decide on a complex framework. The paper addresses the issue by aggregating consumer’s sentiments with customized QoS parameters to choose an optimal service provider. The paper uses the fuzzy Best Worst Method (BWM) to determine the weights of selection criteria and use the fuzzy TOPSIS to handle the uncertain linguistic preference. Analysis results demonstrate the applicability and effectiveness of the framework. |
Keywords | Fuzzy sentiment; Quality of service (QoS); Cloud provider selection; Fuzzy best worst method; Fuzzy TOPSIS; Quality of experience; Service level agreement (SLA) |
Page range | 81-92 |
Year | 01 Jan 2022 |
Book title | Soft Computing and Fuzzy Methodologies in Innovation Management and Sustainability |
Publisher | Springer |
Place of publication | Switzerland |
Edition | 1 |
Series | Lecture Notes in Networks and Systems |
ISBN | 978-3-030-96150-3 |
ISSN | 2367-3370 |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-030-96150-3_6 |
Web address (URL) | https://link.springer.com/chapter/10.1007/978-3-030-96150-3_6 |
Open access | Published as non-open access |
Research or scholarly | Research |
Publisher's version | License All rights reserved File Access Level Controlled |
Output status | Published |
Publication dates | |
Online | 12 Apr 2022 |
Publication process dates | |
Accepted | 16 Feb 2024 |
Deposited | 26 Feb 2024 |
Additional information | © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022. |
https://acuresearchbank.acu.edu.au/item/902xx/aggregating-fuzzy-sentiments-with-customized-qos-parameters-for-cloud-provider-selection-using-fuzzy-best-worst-and-fuzzy-topsis
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