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Selecting skyline services for QoS-aware composition by upgrading MapReduce paradigm

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Abstract

With the development of web technologies and cloud computing, more and more services which provide similar functionality but differ in QoS are deployed on the Internet via cloud platforms. Recently, skyline analysis is adopted to select candidate services with better QoS to facilitate the process of QoS-aware service composition. However, the fast increasing number of services, multiple QoS attributes to be considered, and dynamic service environment pose a big challenge to skyline service selection.

In this paper, we present a parallel skyline service selection method to improve the efficiency by upgrading the MapReduce paradigm. An angle-based dataspace partitioning approach is employed in our MapReduce based skyline service selection. In particular, we explore the dominance power of local skyline services to improve the efficiency of selection, and present two detailed algorithms. To handle the dynamic nature of service environment, we employ Paper-Tape (PT) model which is used to rapidly locate varying services, and present a dynamic skyline service selection algorithm based on PT model. By experimenting over both real and synthetical datasets, we demonstrate the efficiency of our proposed methods.

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Algorithm 1
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Notes

  1. http://webservices.seekda.com.

  2. The definition of P i ,P filter , and P can be found in Algorithms 2 and 3.

  3. http://ccnt.zju.edu.cn:8080.

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Acknowledgements

This research was partially supported by the National Technology Support Program under grant of 2011BAH16B04, the National Natural Science Foundation of China under grant of No. 61173176, Science and Technology Program of Zhejiang Province under grant of 2008C03007, Zhejiang Provincial Natural Science Foundation of China under grant number Y1110591, National High-Tech Research and Development Plan of China under Grant No. 2011AA010501.

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Authors and Affiliations

  1. College of Computer Science & Technology, Zhejiang University, Hangzhou, P.R. China

    Jian Wu, Liang Chen, Yilun Wang & Zhaohui Wu

  2. College of Computing and Information Sciences, Rochester Institute of Technology, Rochester, NY, USA

    Qi Yu

  3. Hangzhou Institute of Service Engineering, Hangzhou Normal University, Hangzhou, P.R. China

    Li Kuang

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  1. Jian Wu
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  2. Liang Chen
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Correspondence to Liang Chen.

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Wu, J., Chen, L., Yu, Q. et al. Selecting skyline services for QoS-aware composition by upgrading MapReduce paradigm. Cluster Comput 16, 693–706 (2013). https://doi.org/10.1007/s10586-012-0240-9

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