Reliability Ranking Prediction for Cloud Services via Skyline

Wei, Xiong; Zhao, Weilong; Li, Bing; Hang, Bo

doi:10.1109/cscloud/edgecom.2018.00021

Cited by 1 publication

(2 citation statements)

References 23 publications

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“…This model improves the cloud user satisfaction in terms of its prediction accuracy and service diversity. In addition, Xiong et al [31] proposed a past cloud user service usage experience-based approach to predict cloud services by means of Skyline. It eliminates costly and tedious web service invocations.…”

Section: A General Cloud Service Selection and Ranking Solutionsmentioning

confidence: 99%

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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%

“…Phase 2: Strength of Opinion In order to assign the opinion of the user review, the root words were matched with existing positive and negative word lists [30,31]. The opinion was assigned based on the occurrence of positive and negative words, i.e., based on the highest number of occurrences.…”

Section: Proposed Systemmentioning

confidence: 99%