A Two-Phase Learning Approach Integrated With Multi-Source Features for Cloud Service QoS Prediction
Document Type
Article
Publication Date
2025
DOI
10.1007/s42524-025-4038-x
Publication Title
Frontiers of Engineering Management
Volume
Article in Press
Pages
11 pp.
Abstract
Quality of Service (QoS) is a key factor for users when choosing cloud services. However, QoS values are often unavailable due to insufficient user evaluations or provider data. To address this, we propose a new QoS prediction method, Multi-source Feature Two-phase Learning (MFTL). MFTL incorporates multiple sources of features influencing QoS and uses a two-phase learning framework to make effective use of these features. In the first phase, coarse-grained learning is performed using a neighborhood-integrated matrix factorization model, along with a strategy for selecting high-quality neighbors for target users. In the second phase, reinforcement learning through a deep neural network is used to capture interactions between users and services. We conducted several experiments using the WS-Dream data set to assess MFTL's performance in predicting response time QoS. The results show that MFTL outperforms many leading QoS prediction methods.
Rights
© 2025 The Authors.
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ORCID
0000-0002-8686-8585 (Wu)
Original Publication Citation
Chen, F. Z., Yang, J., Feng, H. Y., Wu, H., & Li, M. Q. (2025). A two-phase learning approach integrated with multi-source features for cloud service QoS prediction. Frontiers of Engineering Management. Advance online publication. https://doi.org/10.1007/s42524-025-4038-x
Repository Citation
Chen, Fuzan; Yang, Jing; Feng, Haiyang; Wu, Harris; and Li, Minqiang, "A Two-Phase Learning Approach Integrated With Multi-Source Features for Cloud Service QoS Prediction" (2025). Information Technology & Decision Sciences Faculty Publications. 109.
https://digitalcommons.odu.edu/itds_facpubs/109
Included in
Artificial Intelligence and Robotics Commons, Data Science Commons, Technology and Innovation Commons, Theory and Algorithms Commons