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An Online Prediction Framework for Dynamic Service-Generated QoS Big Data

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Database Systems for Advanced Applications (DASFAA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10179))

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Abstract

With the prevalence of service computing, cloud computing, and Internet of Things (IoT), various service compositions are emerging on the Internet based on Service-Oriented Architecture (SOA). To evaluate the performance attribute of these service compositions, dynamic Quality of Service (QoS) data are generated abundantly, named service-generated QoS Big Data. Selecting optimal services to build high quality SOA systems can be based on these data. However, a mass of service-generated QoS data are unknown and it is become a challenge to predict these data. In this paper, we present a framework for service-generated QoS big data prediction, named DSPMF. Under this framework, we present an optimization objective function and employ online stochastic gradient descent algorithm to solve this function. Extensive experiments are conducted to verify the effectiveness and efficiency of our proposed approach.

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References

  1. Assunção, M.D., Calheiros, R.N., Bianchi, S., Netto, M.A., Buyya, R.: Big Data computing and clouds: trends and future directions. J. Parallel Distrib. Comput. 79, 3–15 (2015)

    Article  Google Scholar 

  2. Zheng, Z., Zhu, J., Lyu, M.R.: Service-generated big data and big data-as-a-service: an overview. In: 2013 IEEE International Congress on Big Data (BigData Congress), Santa Clara Marriott, CA, pp. 403–410 (2013)

    Google Scholar 

  3. Suchithra, M., Ramakrishnan, M.: Non functional QoS criterion based web service ranking. In: Suresh, L.P., Panigrahi, B.K. (eds.) Proceedings of the International Conference on Soft Computing Systems. AISC, vol. 398, pp. 79–90. Springer, Heidelberg (2016). doi:10.1007/978-81-322-2674-1_8

    Chapter  Google Scholar 

  4. Shao, L., Zhang, J., Wei, Y., Zhao, J., Xie, B., Mei, H.: Personalized QoS prediction for web services via collaborative filtering. In: IEEE International Conference on Web Services (ICWS 2007), pp. 439–446 (2007)

    Google Scholar 

  5. Wang, X., Zhu, J., Shen, Y.: Network-aware QoS prediction for service composition using geolocation. IEEE Trans. Serv. Comput. 8(4), 630–643 (2015)

    Article  Google Scholar 

  6. Zheng, Z., Ma, H., Lyu, M.R., King, I.: Collaborative web service QoS prediction via neighborhood integrated matrix factorization. IEEE Trans. Serv. Comput. 6(3), 289–299 (2013)

    Article  Google Scholar 

  7. Lo, W., Yin, J., Li, Y., et al.: Efficient web service QoS prediction using local neighborhood matrix factorization. Eng. Appl. Artif. Intell. 38, 14–23 (2015)

    Article  Google Scholar 

  8. Jin, R., Chai, J.Y., Si, L.: An automatic weighting scheme for collaborative filtering. In: The 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pp. 337–344. ACM Press, Sheffield (2004)

    Google Scholar 

  9. Deshpande, M., Karypis, G.: Item-based top-n recommendation. ACM Trans. Inf. Syst. 22(1), 143–177 (2004)

    Article  Google Scholar 

  10. Wu, J., Chen, L., Feng, Y., Zheng, Z., Zhou, M., Wu, Z.: Predicting QoS for service selection by neighborhood-based collaborative filtering. IEEE Trans. Syst. Man Cybern. Part A 43(2), 428–439 (2013)

    Article  Google Scholar 

  11. Zheng, Z., Ma, H., Lyu, M.R., King, I.: QoS-aware web service recommendation by collaborative filtering. IEEE Trans. Serv. Comput. 4(2), 140–152 (2011)

    Article  Google Scholar 

  12. Zheng, Z., Ma, H., Lyu, M.R., King, I.: WSRec: a collaborative filtering based web service recommender system. In: 16th International Conference on Web Services, pp. 437–444, Los Angeles, CA (2009)

    Google Scholar 

  13. Yin, J., Xu, Y.: Personalised QoS-based web service recommendation with service neighbourhood-enhanced matrix factorization. Int. J. Web Grid Serv. 11(1), 39–56 (2015). Special Issue

    Article  Google Scholar 

  14. Lo, W., Yin, J., Deng, S., Li, Y., Wu, Z.: An extended matrix factorization approach for QoS prediction in service selection. In: International Conference on Services Computing (SCC), pp. 162–169 (2012)

    Google Scholar 

  15. Xu, J., Zheng, Z., Lyu, M.R.: Web service personalized QoS prediction via reputation-based matrix factorization. IEEE Trans. Reliab. 65(1), 28–37 (2016)

    Article  Google Scholar 

  16. Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: Advances in Neural Information Processing Systems, pp. 1257–1264 (2015)

    Google Scholar 

  17. Shalev-Shwartz, S.: Online learning and online convex optimization. Found. Trends Mach. Learn. 4(2), 107–194 (2011)

    Article  MATH  Google Scholar 

  18. Bertsekas, D.P.: Incremental gradient, subgradient, and proximal methods for convex optimization: a survey. Optimization 2010(2), 691–717 (2015)

    Google Scholar 

  19. Mairal, J., Bach, F., Ponce, J., Sapiro, G.: Online learning for matrix factorization and sparse coding. J. Mach. Learn. Res. 11, 19–60 (2010)

    MathSciNet  MATH  Google Scholar 

  20. Zhu, F., Honeine, P.: Online kernel nonnegative matrix factorization. Sig. Process. 131, 141–153 (2016)

    Google Scholar 

  21. Zhao, R., Tan, V.Y.F.: Online nonnegative matrix factorization with outliers. IEEE Trans. Sig. Process. 65(3), 555–570 (2017)

    Article  MathSciNet  Google Scholar 

  22. Zheng, Z., Lyu, M.R.: WS-DREAM: a distributed reliability assessment mechanism for web services. In: The 38th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN 2008), Anchorage, Alaska, pp. 392–397 (2008)

    Google Scholar 

  23. Zheng, Z., Lyu, M.R.: Personalized reliability prediction of web services. ACM Trans. Softw. Eng. Methodol. 22(2), 1–28 (2013)

    Article  Google Scholar 

  24. Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Lechevallier, Y., Saporta, G. (eds.) Proceedings of COMPSTAT 2010, pp. 177–186. Physica-Verlag HD, Heidelberg (2010). doi:10.1007/978-3-7908-2604-3_16

    Chapter  Google Scholar 

  25. Zhang, Y., Zheng, Z., Lyu, M.R.: WSPred: a time-aware personalized QoS prediction framework for web services. In: The 22nd IEEE Symposium on Software Reliability Engineering (ISSRE), Los Alamitos, California, pp. 210–219 (2011)

    Google Scholar 

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Acknowledgment

The work described in this paper was supported by the Guangdong High-Level University Project “Green Technologies for Marine Industries”, Guangdong Common Colleges Young Innovative Talents Project, and Shantou University National Fund breeding project (No. NFC16001).

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Authors and Affiliations

  1. Shantou University, Shantou, China

    Jianlong Xu & Changsheng Zhu

  2. Southwest University of Nationalities, Chengdu, China

    Qi Xie

Authors
  1. Jianlong Xu
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  2. Changsheng Zhu
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  3. Qi Xie
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Corresponding author

Correspondence to Jianlong Xu .

Editor information

Editors and Affiliations

  1. Royal Melbourne Institute of Technology , Melbourne, Australia

    Zhifeng Bao

  2. Northwestern University , Evanston, Illinois, USA

    Goce Trajcevski

  3. University of New South Wales , Sydney, New South Wales, Australia

    Lijun Chang

  4. The University of Queensland , Brisbane, Queensland, Australia

    Wen Hua

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Xu, J., Zhu, C., Xie, Q. (2017). An Online Prediction Framework for Dynamic Service-Generated QoS Big Data. In: Bao, Z., Trajcevski, G., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10179. Springer, Cham. https://doi.org/10.1007/978-3-319-55705-2_5

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  • DOI: https://doi.org/10.1007/978-3-319-55705-2_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55704-5

  • Online ISBN: 978-3-319-55705-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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