scholarly journals Normal Cloud Model-Based Algorithm for Multi-Attribute Trusted Cloud Service Selection

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 37644-37652 ◽  
Author(s):  
Yuli Yang ◽  
Rui Liu ◽  
Yongle Chen ◽  
Tong Li ◽  
Yi Tang
Keyword(s):  
Cloud Model ◽  
Service Selection ◽  
Cloud Service ◽  
Model Based

A dynamic cloud service selection model based on trust and SLA in cloud computing

2019 ◽  
Vol 10 (4) ◽  
pp. 334
Author(s):  
Bamei Tao ◽  
Quanwang Wu ◽  
Lei Guo ◽  
Junhao Wen ◽  
Yubiao Wang
Keyword(s):  
Cloud Computing ◽  
Service Selection ◽  
Cloud Service ◽  
Selection Model ◽  
Model Based

scholarly journals Trust-Enhanced Cloud Service Selection Model Based on QoS Analysis

PLoS ONE ◽  
2015 ◽  
Vol 10 (11) ◽  
pp. e0143448 ◽  
Author(s):  
Yuchen Pan ◽  
Shuai Ding ◽  
Wenjuan Fan ◽  
Jing Li ◽  
Shanlin Yang
Keyword(s):  
Service Selection ◽  
Cloud Service ◽  
Selection Model ◽  
Model Based

scholarly journals CC-PSM: A Preference-Aware Selection Model for Cloud Service Based on Consumer Community

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Yan Wang ◽  
Jian-tao Zhou ◽  
Hong-yan Tan
Keyword(s):  
Clustering Algorithm ◽  
Service Selection ◽  
Cloud Service ◽  
Selection Model ◽  
Bipartite Network ◽  
Model Based ◽  
Selection Strategies ◽  
Hierarchical Clustering Algorithm ◽  
Full Consideration

In order to give full consideration to the consumer’s personal preference in cloud service selection strategies and improve the credibility of service prediction, a preference-aware cloud service selection model based on consumer community (CC-PSM) is presented in this work. The objective of CC-PSM is to select a service meeting a target consumer’s demands and preference. Firstly, the correlation between cloud consumers from a bipartite network for service selection is mined to compute the preference similarity between them. Secondly, an improved hierarchical clustering algorithm is designed to discover the consumer community with similar preferences so as to form the trusted groups for service recommendation. In the clustering process, a quantization function called community degree is given to evaluate the quality of community structure. Thirdly, a prediction model based on consumer community is built to predict a consumer’s evaluation on an unknown service. The experimental results show that CC-PSM can effectively partition the consumers based on their preferences and has good effectiveness in service selection applications.

An improved robust heteroscedastic probabilistic neural network based trust prediction approach for cloud service selection

2018 ◽  
Vol 108 ◽  
pp. 339-354 ◽  
Author(s):  
Nivethitha Somu ◽  
Gauthama Raman M.R. ◽  
Kalpana V. ◽  
Kannan Kirthivasan ◽  
Shankar Sriram V.S.
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Service Selection ◽  
Cloud Service ◽  
Trust Prediction ◽  
Prediction Approach
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