An Enhanced QoS Prediction Approach for Service Selection

2011 ◽  
Author(s):  
Liang Chen ◽  
Yipeng Feng ◽  
Jian Wu ◽  
Zibin Zheng
Keyword(s):  
Service Selection ◽  
Qos Prediction ◽  
Prediction Approach

scholarly journals A Factorization Machine-Based QoS Prediction Approach for Mobile Service Selection

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 32961-32970 ◽  
Author(s):  
Mingdong Tang ◽  
Wei Liang ◽  
Yatao Yang ◽  
Jianguo Xie
Keyword(s):  
Service Selection ◽  
Mobile Service ◽  
Qos Prediction ◽  
Factorization Machine ◽  
Prediction Approach

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

Time-aware cloud manufacturing service selection using unknown QoS prediction and uncertain user preferences

2021 ◽  
pp. 1063293X2110195
Author(s):  
Ying Yu ◽  
Shan Li ◽  
Jing Ma
Keyword(s):  
Time Series Prediction ◽  
Service Selection ◽  
Cloud Manufacturing ◽  
Service Evaluation ◽  
User Preferences ◽  
User Preference ◽  
Selection Methods ◽  
Qos Prediction ◽  
Qos Performance ◽  
Time Aware

Selecting the most efficient from several functionally equivalent services remains an ongoing challenge. Most manufacturing service selection methods regard static quality of service (QoS) as a major competitiveness factor. However, adaptations are difficult to achieve when variable network environment has significant impact on QoS performance stabilization in complex task processes. Therefore, dynamic temporal QoS values rather than fixed values are gaining ground for service evaluation. User preferences play an important role when service demanders select personalized services, and this aspect has been poorly investigated for temporal QoS-aware cloud manufacturing (CMfg) service selection methods. Furthermore, it is impractical to acquire all temporal QoS values, which affects evaluation validity. Therefore, this paper proposes a time-aware CMfg service selection approach to address these issues. The proposed approach first develops an unknown-QoS prediction model by utilizing similarity features from temporal QoS values. The model considers QoS attributes and service candidates integrally, helping to predict multidimensional QoS values accurately and easily. Overall QoS is then evaluated using a proposed temporal QoS measuring algorithm which can self-adapt to user preferences. Specifically, we employ the temporal QoS conflict feature to overcome one-sided user preferences, which has been largely overlooked previously. Experimental results confirmed that the proposed approach outperformed classical time series prediction methods, and can also find better service by reducing user preference misjudgments.

Multi-user web service selection based on multi-QoS prediction

2013 ◽  
Vol 16 (1) ◽  
pp. 143-152 ◽  
Author(s):  
Shangguang Wang ◽  
Ching-Hsien Hsu ◽  
Zhongjun Liang ◽  
Qibo Sun ◽  
Fangchun Yang
Keyword(s):  
Web Service ◽  
Service Selection ◽  
Qos Prediction ◽  
Web Service Selection
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