A Cloud Service Selection Model Based on User-Specified Quality of Service Leve

Hsu, Chang-Ling

doi:10.5121/csit.2014.4706

Cited by 7 publications

(3 citation statements)

References 11 publications

(10 reference statements)

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“…The enhanced solution is efficient as a result of the convergence speed and stability contrasted with different propositions. Hsu [15] proposed a cloud service selection model (CloudEval) dependent upon a user-specified QoS level. It evaluates the non-functional properties and selects the optimal services that fulfil both a user-specified service level and goals.…”

Section: A General Cloud Service Selection and Ranking Solutionsmentioning

confidence: 99%

Parameter Reputation Model for Cloud Service Recommendation and Ranking Using Opinion Mining

Govardhanan,

Manikandan,

Arunkumar

et al. 2024

IEEE Access

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Enabling effective cloud service recommendation and ranking model helps cloud users settle on brief choices when they are up against by an enormous number of choices. This helps to improve the cloud users', as well as the service providers', satisfaction levels. In this paper, parameter reputationbased algorithms are proposed to boost the service selection and ranking. The proposed work employs prominentthe parameters of the Service Measurement Index (SMI) standardized by the Cloud Service Measurement Index Consortium (CSMIC). The parameters are systematically analyzed in order to obtain a user review opinion, such as a positive, negative and neutral view about the parameters. Here, both the positive and negative parameter reputations are addressed. The positive parameter reputation increased the cloud users' choice of service selection and ranking. The negative parameter reputation helped the service providers to realize where they are stand and give them a chance to improve their service provisioning in future endeavors. The detailed experimental results confirm that the proposed models enhance the choice of service selection and ranking.

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Section: A General Cloud Service Selection and Ranking Solutionsmentioning

confidence: 99%

Parameter Reputation Model for Cloud Service Recommendation and Ranking Using Opinion Mining

Govardhanan,

Manikandan,

Arunkumar

et al. 2024

IEEE Access

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“…Analytic Hierarchy Process (AHP) based method is used by Sun et al (2013) for selecting the best cloud service provider. A cloud service selection model namely CloudEval, to evaluate the nonfunctional properties and to select the optimal service using Grey Relational Analysis is proposed by Hsu (2014). The Simple Additive Weighting (SAW) method is used to rank the alternatives by Saripalli & Pingali (2011).…”

Section: Related Workmentioning

confidence: 99%

An Integrated Multi-Criteria Decision-Making Model for Cloud Service Provider Selection

Uma¹,

2022

International Journal of Decision Support System Technology

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In the present scenario, it is inevitable for organizations to use the services offered through the cloud. As there are numerous cloud service providers, the users have a vast number of choices to get the desired service. For the long term success of any organization, it is important to choose the right cloud service provider. This paper focuses on proposing a new framework to select the best cloud service provider. While selecting the best service provider, using an appropriate method for finding out the weight of each criterion is very important as it directly impacts the ranking of candidate service providers. In this work, the Full Consistency Method (FUCOM) is employed for finding the weight of each criterion and Multi-Objective Optimization by Ratio Analysis (MOORA) is proposed for ranking the service provider. The integration of FUCOM with MOORA results in computing the reliable values of criteria weight and a simpler procedure for computing the overall score of the service provider. Hence a rational decision making is possible in selecting the best cloud service provider.

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“…In this section, we discussed various previous research work for evaluating, comparing the performance and approaches of different cloud services. Chang-Ling Hsu [3] , proposed a grey relational model for cloud service selection. Grey scale model is designed to identify the user's service level parameters by evaluating the nonfunctional characteristics of cloud services.…”

Section: Related Workmentioning

confidence: 99%

Customized Cloud Service Selection based on Parameterized Weighted Average Model using user Rating

Manikandan¹,

Kousalya²

2016

IJERT

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Most of the individuals and organizations are migrating their work into the cloud environment due to the rapid growth of cloud computing performance. In this context, a lot of cloud service providers are emerging to offer cloud services which in turn resulted, the cloud customers with a number of choices to meet their requirements. From the cloud customers point of view, choosing an appropriate cloud service based on their customized requirement is a cumbersome job. In particular, we propose a parameter weighted average model based on cloud customer feedback ratings for each and every parameters. Cloud customers may give the highest priority to a few parameters from the accessible choice of other QoS parameters, which satisfies their requirements. The proposed model allows the cloud customers to prefer their personalized and customized choices on the basis of their prerequisites and also to help the cloud service providers to realize the requirement intention of the cloud customers and hence to improve the quality of services.

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A Cloud Service Selection Model Based on User-Specified Quality of Service Leve

Abstract: ABSTRACT

Cited by 7 publications

References 11 publications

Parameter Reputation Model for Cloud Service Recommendation and Ranking Using Opinion Mining

Parameter Reputation Model for Cloud Service Recommendation and Ranking Using Opinion Mining

An Integrated Multi-Criteria Decision-Making Model for Cloud Service Provider Selection

Customized Cloud Service Selection based on Parameterized Weighted Average Model using user Rating

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