A two-phase learning approach integrated with multi-source features for cloud service QoS prediction  

作　　者：Fuzan CHEN Jing YANG Haiyang FENG Harris WU Minqiang LI 

机构地区：[1]College of Management and Economics,Tianjin University,Tianjin 300072,China [2]Department of Information Technology and Decision Sciences,Old Dominion University,Norfolk,VA 23529,USA

出　　处：《Frontiers of Engineering Management》2025年第1期117-127,共11页工程管理前沿(英文版)

基　　金：National Natural Science Foundation of China(Grants Nos.72394373,72231004,72022012,and 71971153).

摘　　要：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 experi-ments 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.

关 键 词：cloud service QoS prediction matrix factorization deep neural network 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]