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

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.

Graphical abstract

Keywords

cloud service / QoS prediction / matrix factorization / deep neural network

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RIGHTS & PERMISSIONS

The Author(s). This article is published with open access at link.springer.com and journal.hep.com.cn