Colbar: A collaborative location-based regularization framework for QoS prediction
Information Sciences: an International Journal
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With the rapid growth of Web services in recent years, the optimal service selection from functionally-equivalent service candidates has become more critical for building high quality service-oriented systems. To provide accurate QoSvalues for service selection, user-side QoS prediction thus becomes an important research problem. Although collaborative filtering based prediction approaches have been studied in several previous works, these methods suffer from the limitation of the sparsity of available historical QoS data, which greatly degrades the prediction accuracy. To address this problem, this paper proposes a Web service positioning (WSP)framework for response time prediction, which is one of the most important QoS properties. In our approach, a small set of landmarks are deployed to periodically monitor the response times of the Web service candidates and provide references to the numerous service users. By combining the advantages of network coordinate based approaches and collaborative filtering based approaches, the response times between users and Web services can be accurately predicted using their corresponding Euclidean distances. Extensive experiments are conducted based on our real-world QoS dataset collected on PlanetLab, comprising about 359,400 response time values from 200 users on 1,597 Web services. The experimental results show that our WSP approach outperforms the other existing approaches, especially when the historical data is sparse.