Cloud Manufacturing Service Selection Model Based on Adaptive Variable Evaluation Metrics

2016 ◽  
pp. 13-19 ◽  
Author(s):  
Jin Cui ◽  
Lei Ren ◽  
Lin Zhang
Keyword(s):  
Service Selection ◽  
Cloud Manufacturing ◽  
Selection Model ◽  
Evaluation Metrics ◽  
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

Service Selection Based on Rule and Statistics Model in WoT Smart Home

2014 ◽  
Vol 681 ◽  
pp. 244-248
Author(s):  
Dan Wei ◽  
Chun Hong Zhang ◽  
Xin Ning Zhu
Keyword(s):  
Smart Home ◽  
Selection Process ◽  
Service Selection ◽  
Research Area ◽  
Selection Model ◽  
Web Of Things ◽  
Combination Model ◽  
Model Based ◽  
Rule Model

As the development of WoT(Web of Things), a large mount of services emerge. How to make full use of these services is a hot research area nowadays. This paper focuses on making these services more valuable in WoT Smart Home scenario. In this paper, we propose a model called service selection model based on rule and statistics into WoT Smart Home scenario. It is a combination of rule model and statistics model. It not only automatizes the service selection process by adopting statistics algorithm, but also dramatically improves the accuracy of this process by combining rule and statistics. Further more, it saves efforts of the annotation of service rules greatly. We carry out some experiments to verify the efficiency of this model by comparing with rule model and statistics respectively. The results show that the combination model can efficiently and precisely select services.

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.

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