Abstract
This paper proposes neighborhood-based approach for QoS-prediction of cloud services by taking advantages of collaborative intelligence. Different from heuristic collaborative filtering and matrix-factorization, we set a formal neighborhood-based prediction framework which allows an efficient global optimization scheme, and then exploits different baseline estimate components to improve predictive performance. To validate our methods, a large-scale QoS-specific dataset which consists of invocation records from 339 service users on 5,825 web services on a world-scale distributed network is used. Experimental results show that the learned neighborhood-based models can overcome existing difficulties of heuristic collaborative filtering methods and achieve superior performance than state-of-the-art prediction methods.
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Acknowledgements
This work is supported by the Special Funds for Middle-aged and Young Core Instructor Training Program of Yunnan University, the Applied Basic Research Project of Yunnan Province (2013FB009,2014FA023), the Program for Innovative Research Team in Yunnan University (XT412011), and the National Natural Science Foundation of China (61562090,61562092,61472345).
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Wu, H., He, J., Li, B., Pei, Y. (2016). Personalized QoS Prediction of Cloud Services via Learning Neighborhood-Based Model. In: Guo, S., Liao, X., Liu, F., Zhu, Y. (eds) Collaborative Computing: Networking, Applications, and Worksharing. CollaborateCom 2015. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 163. Springer, Cham. https://doi.org/10.1007/978-3-319-28910-6_10
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DOI: https://doi.org/10.1007/978-3-319-28910-6_10
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