A personalized clustering-based and reliable trust-aware QoS prediction approach for cloud service recommendation in cloud manufacturing

2019 ◽  
Vol 174 ◽  
pp. 43-56 ◽  
Author(s):  
Jian Liu ◽  
Youling Chen
Keyword(s):  
Cloud Service ◽  
Cloud Manufacturing ◽  
Service Recommendation ◽  
Qos Prediction ◽  
Prediction Approach

scholarly journals End-to-end QoS computation for vertical service composition in the cloud

2021 ◽  
Author(s):  
Raed Karim
Keyword(s):  
Service Composition ◽  
Cloud Service ◽  
Cloud Services ◽  
Prior History ◽  
Functional Requirements ◽  
Active System ◽  
Qos Prediction ◽  
Cloud Providers ◽  
End To End ◽  
Prediction Approach

Cloud services are designed to provide users with different computing models such as software-as-a-Services (SaaS), Infrastructure-as-a-Service (IaaS), Data-as-a-Service (DaaS), and other IT related services (denoted as XaaS). Easy, scalable and on-demand cloud services are offered by cloud providers to users. With the prevalence of different types of cloud services, the task of selecting the best cloud service solution has become more and more challenging. Cloud service solutions are offered through a collaboration of different cloud services at different cloud layers. This type of collaborations is denoted as vertical service composition. Quality of Service (QoS) properties are used as differentiating factors for selecting the best services among functionally equivalent services. In this thesis, we introduce a new service selection framework for the cloud which vertically matches services offered by different cloud providers based on users’ end-to-end QoS requirements. Functional requirements can be satisfied by the required cloud service (software service, platform service, etc) alone. However, users’ QoS requirements must be satisfied using all involved cloud services in a service composition. Therefore, in order to select the best cloud service compositions for users, QoS values of these compositions must be end-to-end. To tackle the problem of computing unknown end-to-end QoS values of vertical cloud service compositions for target users (for whom these values are computed), we propose two strategies: QoS mapping and aggregation and QoS prediction. The former deals with new cloud service compositions with no prior history. Using this strategy, we can map users’ QoS requirements onto different cloud layers and then we aggregate QoS values guaranteed by cloud providers to estimate end-to-end QoS values. The latter deals with cloud service compositions for which QoS data have been recorded in an active system. Using the QoS prediction strategy, we utilize historical QoS data of previously invoked service compositions and other service and user information to predict end-to-end QoS values. The presented experimental results demonstrate the importance of considering vertically composed cloud services when computing end-to-end QoS values as opposed to traditional prediction approaches. Our QoS prediction approach outperforms other prediction approaches in terms of the prediction accuracy by at least 20%.

A Spatial-Temporal QoS Prediction Approach for Time-aware Web Service Recommendation

2016 ◽  
Vol 10 (1) ◽  
pp. 1-25 ◽  
Author(s):  
Xinyu Wang ◽  
Jianke Zhu ◽  
Zibin Zheng ◽  
Wenjie Song ◽  
Yuanhong Shen ◽  
...  
Keyword(s):  
Web Service ◽  
Service Recommendation ◽  
Qos Prediction ◽  
Prediction Approach ◽  
Time Aware

TAP: A personalized trust-aware QoS prediction approach for web service recommendation

2017 ◽  
Vol 115 ◽  
pp. 55-65 ◽  
Author(s):  
Kai Su ◽  
Bin Xiao ◽  
Baoping Liu ◽  
Huaiqiang Zhang ◽  
Zongsheng Zhang
Keyword(s):  
Web Service ◽  
Service Recommendation ◽  
Qos Prediction ◽  
Prediction Approach

QoS Prediction Approach for Web Service Recommendation

2010 ◽  
Vol 20-23 ◽  
pp. 987-991
Author(s):  
Zu Qin Chen ◽  
Ji Ke Ge
Keyword(s):  
Web Service ◽  
Service Recommendation ◽  
High Quality ◽  
Qos Prediction ◽  
Prediction Approach

This paper proposes a QoS prediction approach used to recommend web service to solve the problem of choosing high quality web service. This approach bases on other users’ experience. According to the different feeling of different user to the same service, and the different similarity among the QoS of different services, the approach first divides the users and services into several modes, and then computes the means of one mode to predict the QoS of a service that the user never used before. Experiment simulation show that the approach is operable to some extent.

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