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Open Access

A Time-Aware Dynamic Service Quality Prediction Approach for Services

Department of Management, Hefei University, Hefei 230601, China.
School of Computer Science and Technology, and also with the Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei 230601, China.
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Abstract

Dynamic Quality of Service (QoS) prediction for services is currently a hot topic and a challenge for research in the fields of service recommendation and composition. Our paper addresses the problem with a Time-aWare service Quality Prediction method (named TWQP), a two-phase approach with one phase based on historical time slices and one on the current time slice. In the first phase, if the user had invoked the service in a previous time slice, the QoS value for the user calling the service on the next time slice is predicted on the basis of the historical QoS data; if the user had not invoked the service in a previous time slice, then the Covering Algorithm (CA) is applied to predict the missing values. In the second phase, we predict the missing values for the current time slice according to the results of the previous phase. A large number of experiments on a real-world dataset, WS-Dream, show that, when compared with the classical QoS prediction algorithms, our proposed method greatly improves the prediction accuracy.

Keywords

dynamic Quality of Service (QoS) predictiontime-awareservice recommendation

References

[1]
J. W. Yin, Y. Tang, W. Lo, and L. Lo, From big data to great services, in IEEE International Congress on Big Data (Big Data Congress 2016), San Francisco, CA, USA, 2016, pp. 165–172.
Crossref
[2]
Y. W. Zhang, G. M. Cui, S. G. Deng, F. F. Chen, Y. Wang, and Q. He, Efficient query of quality correlation for service composition, IEEE Transactions on Services Computing, .
Crossref Google Scholar
[3]
Q. He, J. Han, F. F. Chen, Y. C. Wang, R. Vasa, Y. Yang, and H. Jin, QoS-aware service selection for customizable multi-tenant service-based systems: Maturity and approaches, in IEEE 8th International Conference on Cloud Computing, New York, NY, USA, 2015, pp. 237–244.
Crossref
[4]
Y. W. Zhang, G. M. Cui, Y. Wang, X. Guo, and S. Zhao, An optimization algorithm for services composition based on an improved FOA, Tsinghua Science and Technology, vol. 20, no. 1, pp. 90–99, 2015.
Crossref Google Scholar
[5]
Y. W. Zhang, G. M. Cui, S. Zhao, and J. Tang, IFOA4WSC: A quick and effective algorithm for QoS aware service composition, International Journal of Web and Grid Services, vol. 12, no. 1, pp. 81–108, 2016.
Crossref Google Scholar
[6]
S. G. Deng, H. Y. Wu, D. N. Hu, and J. L. Zhao, Service selection for composition with QoS correlations, IEEE Transactions on Services Computing, vol. 9, no. 2, pp. 291–303, 2016.
Crossref Google Scholar
[7]
Y. Yu, J. Chen, S. Q. Lin, and Y. Wang, A dynamic QoS aware logistics service composition algorithm based on social network, IEEE Transactions on Emerging Topics in Computing, vol. 2, no. 4, pp. 399–410, 2014.
Crossref Google Scholar
[8]
F. Dahan, K. El Hindi, and A. Ghoneim, Enhanced artificial bee colony algorithm for QoS-aware web service selection problem, Computing, vol. 99, no. 5, pp. 507–517, 2017.
Crossref Google Scholar
[9]
Y. Guo, S. G. Wang, K. S. Wong, and M. H. Kim, Skyline service selection approach based on QoS prediction, International Journal of Web and Grid Services, vol. 13, no. 4, pp. 425–447, 2017.
Crossref Google Scholar
[10]
Y. S. Xu, J. W. Yin, S. G. Deng, N. N. Xiong, and J. B. Huang, Context-aware QoS prediction for web service recommendation and selection, Expert Systems with Applications, vol. 53, pp. 75–86, 2016.
Crossref Google Scholar
[11]
K. Su, B. Xiao, B. P. Liu, H. Q. Zhang, and Z. S. Zhang, TAP: A personalized trust-aware QoS prediction approach for web service recommendation, Knowledge-Based Systems, vol.115, pp. 55–65, 2017.
Crossref Google Scholar
[12]
S. G. Deng, D. G. Wang, Y. Li, B. Cao, J. W. Yin, Z. H. Wu, and M. C. Zhou, A recommendation system to facilitate business process modeling, IEEE Transactions on Cybernetics, vol. 47, no. 6, pp. 1380–1394, 2017.
Crossref Google Scholar
[13]
Y. W. Zhang, Y. Y. Zhou, F. T. Wang, Z. Sun, and Q. He, Service recommendation based on quotient space granularity analysis and covering algorithm on Spark, Knowledge-Based Systems, vol. 147, pp. 25–35, 2018.
Crossref Google Scholar
[14]
X. Chen, Z. B. Zheng, Q. Yu, and M. R. Lyu, Web service recommendation via exploiting location and QoS information, IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 7, pp. 1913–1924, 2014.
Crossref Google Scholar
[15]
Z. B. Zheng, H. Ma, M. R. Lyu, and I. King, QoS-aware web service recommendation by collaborative filtering, IEEE Transactions on Services Computing, vol. 4, no. 2, pp. 140–152, 2011.
Crossref Google Scholar
[16]
L. Si and R. Jin, Flexible mixture model for collaborative filtering, in Proceedings of the 20th International Conference on Machine Learning (ICML-03), Washington, DC, USA, 2003, pp. 704–711.
[17]
X. Luo, M. C. Zhou, Y. N. Xia, and Q. S. Zhu, An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems, IEEE Transactions on Industrial Informatics, vol. 10, no. 2, pp. 1273–1284, 2014.
Crossref Google Scholar
[18]
L. S. Shao, J. Zhang, Y. Wei, J. F. Zhao, B. Xie, and H. Mei, Personalized QoS prediction for web services via collaborative filtering, in IEEE International Conference on Web Services (ICWS 2007), Salt Lake City, UT, USA, 2007, pp. 439–446.
Crossref
[19]
G. Linden, B. Smith, and J. York, Amazon.com recommendations: Item-to-item collaborative filtering, IEEE Internet Computing, vol. 7, no. 1, pp. 76–80, 2003.
Crossref Google Scholar
[20]
Z. B. Zheng, H. Ma, M. R. Lyu, and I. King, WSRec: A collaborative filtering based web service recommender system, in IEEE International Conference on Web Services (ICWS 2009), Los Angeles, CA, USA, 2009, pp. 437–444.
Crossref
[21]
L. Yao, Q. Z. Sheng, A. H. H. Ngu, J. Yu, and A. Segev, Unified collaborative and content-based web service recommendation, IEEE Transactions on Services Computing, vol. 8, no. 3, pp. 453–466, 2015.
Crossref Google Scholar
[22]
M. Zhang, X. D. Liu, R. C. Zhang, and H. L. Sun, A web service recommendation approach based on QoS prediction using fuzzy clustering, in 2012 IEEE Ninth International Conference on Services Computing (SCC), Honolulu, HI, USA, 2012, pp. 138–145.
Crossref
[23]
Z. B. Zheng, H. Ma, M. R. Lyu, and I. King, Collaborative web service QoS prediction via neighborhood integrated matrix factorization, IEEE Transactions on Services Computing, vol. 6, no. 3, pp. 289–299, 2013.
Crossref Google Scholar
[24]
C. Y. Yu and L. P. Huang, CluCF: A clustering CF algorithm to address data sparsity problem, Service Oriented Computing and Applications, vol. 11, no. 1, pp. 33–45, 2017.
Crossref Google Scholar
[25]
L. Zhang and B. Zhang, A geometrical representation of McCulloch-pitts neural model and its applications, IEEE Transactions on Neural Networks, vol. 10, no. 4, pp. 925–929, 1999.
Crossref Google Scholar
[26]
T. Wu, L. Zhang, and Y. P. Zhang, Kernel covering algorithm for machine learning, Chinese Journal of Computers, vol. 28, no. 8, pp. 1295–1301, 2005.
Google Scholar
[27]
L. Zhang and B. Zhang, A geometrical representation of M-P neural model and its applications, Chinese Journal of Software, vol. 9, no. 5, pp. 334–338, 1998.
Google Scholar
[28]
Y. L. Zhang, Z. B. Zheng, and M. R. Lyu, WSPred: A time-aware personalized QoS prediction framework for services, in 2011 22nd IEEE International Symposium on Software Reliability Engineering (ISSRE), Hiroshima, Japan, 2011, pp. 210–219.
Crossref
[29]
Z. Zheng and M. R. Lyu, Personalized reliability prediction of web services, ACM Transactions on Software Engineering and Methodology (TOSEM), vol. 22, no. 2, pp. 1–25, 2013.
Crossref Google Scholar
[30]
C. Wu, W. Qiu, Z. Zheng, X. Wang, and X. Yang, QoS prediction of web services based on two-phase k-means clustering, in IEEE International Conference on Web Services (ICWS 2015), New York, NY, USA, 2015, pp. 161–168.
Crossref
Tsinghua Science and Technology
Volume 25 Issue 2,
April 2020
Pages 227-238
DOI: 10.26599/TST.2019.9010007
Cite this article:
Jin Y, Guo W, Zhang Y. A Time-Aware Dynamic Service Quality Prediction Approach for Services. Tsinghua Science and Technology, 2020, 25(2): 227-238. https://doi.org/10.26599/TST.2019.9010007

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Received: 05 October 2018
Revised: 25 December 2018
Accepted: 11 March 2019
Published: 02 September 2019
© The author(s) 2020

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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