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 predict...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.展开更多
基金National Natural Science Foundation of China(Grants Nos.72394373,72231004,72022012,and 71971153).
文摘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.
