Cloud Service Provider Evaluation System Using Fuzzy Rough Set Technique

Anjana, Parwat Singh; Badiwal, Priyanka; Wankar, Rajeev; Rao, C. Raghavendra

doi:10.1109/sose.2019.00033

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…The heterogeneity and fuzziness of the QoS parameters was addressed through entropy weights and GRA-ELECTRE III method was used for overall evaluation of CSs. The work in [2] also employed fuzzy rough sets and weighted Euclidean distance to rank CSs. The work in [29] proposed a method employing matter element extension for ranking CSs.…”

Section: B Rank Cloud Services Using Vikor Methodsmentioning

confidence: 99%

Evaluating and Ranking Cloud IaaS, PaaS and SaaS Models Based on Functional and Non-Functional Key Performance Indicators

Nadeem

2022

IEEE Access

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With the recent maturity of Cloud computing technology and the flexibility of the Cloud services, the offering of Cloud services has spread exponentially. Different Cloud service providers offer their services highlighting different features of their services. Because of the diversity of Cloud services and their highlighted features, the choice of the most suitable Cloud service is a complex problem for the Cloud users. Many Cloud users are unable to identify the Cloud services that best suit their needs and thus choose an unsuitable Cloud service, which results in financial loses as well as time delays. To this end, in this study, we propose an efficient three layered framework for evaluating and ranking IaaS, PaaS and SaaS Cloud services. We identified the functional and non-functional key performance indicators (KPIs) for Cloud services from 6 KPI classes. We classified these KPIs with respect to their types and criticality so that the Cloud user can easily choose according to his needs. The relative importance of the KPIs was determined using CRITIC method. We combined the KPIs values and their relative importance for an overall evaluation and ranking of the Cloud services using Vikor method. A case study is also presented for a step by step demonstration of the proposed method.

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Section: B Rank Cloud Services Using Vikor Methodsmentioning

confidence: 99%

Evaluating and Ranking Cloud IaaS, PaaS and SaaS Models Based on Functional and Non-Functional Key Performance Indicators

Nadeem

2022

IEEE Access

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“…Existing Cloud Monitoring Solutions (CMS) is used to dynamic QoS attributes tracking related to the virtualized cloud resources which can be used to improve CSP performance. CMS used to detect the performance fluctuations and account for the SLA breaches of QoS attributes [38], [39]. Existing CMS such as Amazon Cloud Watch [40] and Private Cloud Monitoring System (PCMONS) [41] can also be used.…”

Section: Cloud Service Provider Selection Based On Computed Trust mentioning

confidence: 99%

Enhanced QoS-Based Model for Trust Assessment in Cloud Computing Environment

et al. 2020

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Trust management becomes an urgent requirement in the cloud environment and a trust relationship between service user and service provider is required. Trust is the estimation of the ability of cloud resources in completing a task based on some criteria such as availability, reliability, and resource processing power. In this paper, an enhanced QoS-based model for evaluating the trustworthiness of the cloud provider is introduced. The proposed model calculates the accumulative trust value which is updated dynamically at each transaction and reflects the current or latest transaction of the provider in the cloud. The trustworthiness of a cloud resource is evaluated based on its provider reputation history from user feedback ratings based on the covariance mathematical technique to evaluate the credibility of the user's feedback. The trustworthiness of a cloud resource is also evaluated by calculating the computing power of resources at run-time. Experimental results confirm the effect of user opinion and resources processing speed on trust value calculation, which in turn assesses the trustworthiness of the cloud provider. The simulation has been performed using the CloudSim with the platform Eclipse for developing the proposed model.

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Survey on Services and Support in Cloud Computing Environment

Pandey¹,

Singh²

2022

Lecture Notes in Electrical Engineering

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Cloud Service Provider Evaluation System Using Fuzzy Rough Set Technique

Cited by 4 publications

References 24 publications

Evaluating and Ranking Cloud IaaS, PaaS and SaaS Models Based on Functional and Non-Functional Key Performance Indicators

Evaluating and Ranking Cloud IaaS, PaaS and SaaS Models Based on Functional and Non-Functional Key Performance Indicators

Enhanced QoS-Based Model for Trust Assessment in Cloud Computing Environment

Survey on Services and Support in Cloud Computing Environment

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