Service Selection for Mobile Service Orchestration

2014 ◽  
Author(s):  
Longtao Huang ◽  
Shuiguang Deng
Keyword(s):  
Service Selection ◽  
Mobile Service ◽  
Service Orchestration ◽  
Selection For

Mobile Service Selection for Composition: An Energy Consumption Perspective

2017 ◽  
Vol 14 (3) ◽  
pp. 1478-1490 ◽  
Author(s):  
Shuiguang Deng ◽  
Hongyue Wu ◽  
Wei Tan ◽  
Zhengzhe Xiang ◽  
Zhaohui Wu
Keyword(s):  
Energy Consumption ◽  
Service Selection ◽  
Mobile Service ◽  
Selection For

scholarly journals Privacy-Aware Multidimensional Mobile Service Quality Prediction and Recommendation in Distributed Fog Environment

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Wenwen Gong ◽  
Lianyong Qi ◽  
Yanwei Xu
Keyword(s):  
Service Selection ◽  
Locality Sensitive Hashing ◽  
Mobile Service ◽  
Service Recommendation ◽  
User Privacy ◽  
Service Usage ◽  
Selection Decisions ◽  
Wide Range ◽  
Cross Platform ◽  
Computational Resources

With the ever-increasing popularity of mobile computing technology, a wide range of computational resources or services (e.g., movies, food, and places of interest) are migrating to the mobile infrastructure or devices (e.g., mobile phones, PDA, and smart watches), imposing heavy burdens on the service selection decisions of users. In this situation, service recommendation has become one of the promising ways to alleviate such burdens. In general, the service usage data used to make service recommendation are produced by various mobile devices and collected by distributed edge platforms, which leads to potential leakage of user privacy during the subsequent cross-platform data collaboration and service recommendation process. Locality-Sensitive Hashing (LSH) technique has recently been introduced to realize the privacy-preserving distributed service recommendation. However, existing LSH-based recommendation approaches often consider only one quality dimension of services, without considering the multidimensional recommendation scenarios that are more complex but more common. In view of this drawback, we improve the traditional LSH and put forward a novel LSH-based service recommendation approach named SerRecmulti-qos, to protect users’ privacy over multiple quality dimensions during the distributed mobile service recommendation process.

Mobility-Enabled Service Selection for Composite Services

2016 ◽  
Vol 9 (3) ◽  
pp. 394-407 ◽  
Author(s):  
Shuiguang Deng ◽  
Longtao Huang ◽  
Daning Hu ◽  
J. Leon Zhao ◽  
Zhaohui Wu
Keyword(s):  
Service Selection ◽  
Composite Services ◽  
Selection For

Modeling and Simulation of QoS-aware Web Service Selection for Provider Profit Maximization

SIMULATION ◽  
2007 ◽  
Vol 83 (1) ◽  
pp. 93-106 ◽  
Author(s):  
Dimitrios Tsesmetzis ◽  
Ioanna Roussaki ◽  
Efstathios Sykas
Keyword(s):  
Modeling And Simulation ◽  
Web Service ◽  
Profit Maximization ◽  
Service Selection ◽  
Web Service Selection ◽  
Selection For

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
Sign in / Sign up

Export Citation Format

Share Document