A Probability Distribution and Location-aware ResNet Approach for QoS Prediction

Authors

  • Wenyan Zhang School of Big Data & Software Engineering, ChongQing University, Chongqing, 400044, China https://orcid.org/0000-0001-5890-5775
  • Ling Xu School of Big Data & Software Engineering, ChongQing University, Chongqing, 400044, China https://orcid.org/0000-0002-7203-511X
  • Meng Yan School of Big Data & Software Engineering, ChongQing University, Chongqing, 400044, China
  • Ziliang Wang School of Big Data & Software Engineering, ChongQing University, Chongqing, 400044, China https://orcid.org/0000-0001-7534-0059
  • Chunlei Fu School of Big Data & Software Engineering, ChongQing University, Chongqing, 400044, China https://orcid.org/0000-0002-2690-0475

DOI:

https://doi.org/10.13052/jwe1540-9589.20415

Keywords:

QoS prediction, deep learning, ResNet, probability distribution

Abstract

In recent years, the number of online services has grown rapidly, invoking the required services through the cloud platform has become the primary trend. How to help users choose and recommend high-quality services among huge amounts of unused services has become a hot issue in research. Among the existing QoS prediction methods, the collaborative filtering (CF) method can only learn low-dimensional linear characteristics, and its effect is limited by sparse data. Although existing deep learning methods could capture high-dimensional nonlinear features better, most of them only use the single feature of identity, and the problem of network deepening gradient disappearance is serious, so the effect of QoS prediction is unsatisfactory. To address these problems, we propose an advanced probability distribution and location-aware ResNet approach for QoS Prediction (PLRes). This approach considers the historical invocations probability distribution and location characteristics of users and services, and first uses the ResNet in QoS prediction to reuses the features, which alleviates the problems of gradient disappearance and model degradation. A series of experiments are conducted on a real-world web service dataset WS-DREAM. At the density of 5%–30%, the experimental results on both QoS attribute response time and throughput indicate that PLRes performs better than the existing five state-of-the-art QoS prediction approaches.

Downloads

Download data is not yet available.

Author Biographies

Wenyan Zhang, School of Big Data & Software Engineering, ChongQing University, Chongqing, 400044, China

Wenyan Zhang received her bachelor degree from Chongqing University, China in 2019 and went on to pursue her master degree at Chongqing University in the same year. Her research interests are in service discovery and recommendation.

Ling Xu, School of Big Data & Software Engineering, ChongQing University, Chongqing, 400044, China

Ling Xu is an Associate Professor at the School of Big Data & Software Engineering, Chongqing Univeristy, China. She received her B.S. degree in Hefei University of Technology in 1998, and her M.S. degree in software engineering in 2004. She received her Ph.D. degree in Computer Application from Chongqing University, P.R. China in 2009. Her research interests include mining software repositories, bug rediction and localization.

Meng Yan, School of Big Data & Software Engineering, ChongQing University, Chongqing, 400044, China

Meng Yan is a post-doctoral research fellow in College of Computer Science and Technology, Zhejiang University. He received his PhD degree in June 2017 from the School of Software Engineering, Chongqing University. His currently research focuses on how to improve developer’s productivity, how to improve software quality and how to reduce the effort during software development by analyzing rich software repository data. More information at: https://yanmeng.github.io/

Ziliang Wang, School of Big Data & Software Engineering, ChongQing University, Chongqing, 400044, China

Ziliang Wang received the B.S. degree from Nanchang Hangkong University, Jiangxi, China, in 2017. He is currently pursuing the Ph.D degree in software engineering in Chongqing University, Chongqing, China. His current research interests include service computing, smart city and system structure.

Chunlei Fu, School of Big Data & Software Engineering, ChongQing University, Chongqing, 400044, China

Chunlei Fu is currently a senior engineer at School of Big Data & Software Engineering, Chongqing University. He received his PhD degree at in School of Automation, Chongqing University, China, in 2014. He received a postdoctoral training at the school of computer science in Chongqing University, studying Knowledge-Based Software Engineering. His major research interests include Knowledge Graph, Service Computing, and Software Engineering.

References

D.A. Adeniyi, Z. Wei, and Y. Yongquan. Automated web usage data mining and recommendation system using k-nearest neighbor (knn) classification method. Applied Computing and Informatics, 12(1):90–108, 2016.

E. Ahmad, M. Alaslani, F. R. Dogar, and B. Shihada. Location-aware, context-driven qos for iot applications. IEEE Systems Journal, 14(1):232–243, 2020.

Weihong Cai, Xin Du, and Jianlong Xu. A personalized qos prediction method for web services via blockchain-based matrix factorization. Sensors, 19(12):2749, 2019.

Soumi Chattopadhyay and Ansuman Banerjee. Qos value prediction using a combination of filtering method and neural network regression. In Sami Yangui, Ismael Bouassida Rodriguez, Khalil Drira, and Zahir Tari, editors, Service-Oriented Computing – 17th International Conference, ICSOC 2019, Toulouse, France, October 28–31, 2019, Proceedings, volume 11895 of Lecture Notes in Computer Science, pages 135–150. Springer, 2019.

K. Chen, H. Mao, X. Shi, Y. Xu, and A. Liu. Trust-aware and location-based collaborative filtering for web service qos prediction. In 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC), volume 2, pages 143–148, 2017.

Zhen Chen, Limin Shen, Dianlong You, Chuan Ma, and Feng Li. A location-aware matrix factorisation approach for collaborative web service qos prediction. Int. J. Comput. Sci. Eng., 19(3):354–367, 2019.

T. Cheng, J. Wen, Q. Xiong, J. Zeng, W. Zhou, and X. Cai. Personalized web service recommendation based on qos prediction and hierarchical tensor decomposition. IEEE Access, 7:62221–62230, 2019.

Shuai Ding, Chengyi Xia, Qiong Cai, Kaile Zhou, and Shanlin Yang. Qos-aware resource matching and recommendation for cloud computing systems. Appl. Math. Comput., 247:941–950, 2014.

Feng-Jian Wang, Yen-Hao Chiu, Chia-Ching Wang, Kuo-Chan Huang. A Referral-Based QoS Prediction Approach for Service- Based Systems. Journal of Computers, 13(2):176–186, 2018.

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. CoRR, abs/1512.03385, 2015.

Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. Neural collaborative filtering. In Rick Barrett, Rick Cummings, Eugene Agichtein, and Evgeniy Gabrilovich, editors, Proceedings of the 26th International Conference on World Wide Web, WWW 2017, Perth, Australia, April 3–7, 2017, pages 173–182. ACM, 2017.

W. Hong, N. Zheng, Z. Xiong, and Z. Hu. A parallel deep neural network using reviews and item metadata for cross-domain recommendation. IEEE Access, 8:41774–41783, 2020.

I. M. A. Jawarneh, P. Bellavista, A. Corradi, L. Foschini, R. Montanari, J. Berrocal, and J. M. Murillo. A pre-filtering approach for incorporating contextual information into deep learning based recommender systems. IEEE Access, 8:40485–40498, 2020.

Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In Yoshua Bengio and Yann LeCun, editors, 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7–9, 2015, Conference Track Proceedings, 2015.

Hamza Labbaci, Brahim Medjahed, and Youcef Aklouf. A deep learning approach for long term qos-compliant service composition. In E. Michael Maximilien, Antonio Vallecillo, Jianmin Wang, and Marc Oriol, editors, Service-Oriented Computing – 15th International Conference, ICSOC 2017, Malaga, Spain, November 13–16, 2017, Proceedings, volume 10601 of Lecture Notes in Computer Science, pages 287–294. Springer, 2017.

Shun Li, Junhao Wen, Fengji Luo, Tian Cheng, and Qingyu Xiong. A location and reputation aware matrix factorization approach for personalized quality of service prediction. In 2017 IEEE International Conference on Web Services (ICWS), pages 652–659. IEEE, 2017.

Shun Li, Junhao Wen, and Xibin Wang. From reputation perspective: A hybrid matrix factorization for qos prediction in location-aware mobile service recommendation system. Mobile Information Systems, 2019:8950508:1–8950508:12, 2019.

Greg Linden, Brent Smith, and Jeremy York. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Comput., 7(1):76–80, 2003.

An Liu, Xindi Shen, Zhixu Li, Guanfeng Liu, Jiajie Xu, Lei Zhao, Kai Zheng, and Shuo Shang. Differential private collaborative web services qos prediction. World Wide Web, 22(6):2697–2720, 2019.

Wei Lo, Jianwei Yin, ShuiGuang Deng, Ying Li, and Zhaohui Wu. Collaborative web service qos prediction with location-based regularization. In Carole A. Goble, Peter P. Chen, and Jia Zhang, editors, 2012 IEEE 19th International Conference on Web Services, Honolulu, HI, USA, June 24–29, 2012, pages 464–471. IEEE Computer Society, 2012.

Ruslan Salakhutdinov and Andriy Mnih. Probabilistic matrix factorization. In John C. Platt, Daphne Koller, Yoram Singer, and Sam T. Roweis, editors, Advances in Neural Information Processing Systems 20, Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 3–6, 2007, pages 1257–1264. Curran Associates, Inc., 2007.

Qibo Sun, Lubao Wang, Shangguang Wang, You Ma, and Ching-Hsien Hsu. Qos prediction for web service in mobile internet environment. New Rev. Hypermedia Multim., 22(3):207–222, 2016.

Mingdong Tang, Yechun Jiang, Jianxun Liu, and Xiaoqing (Frank) Liu. Location-aware collaborative filtering for qos-based service recommendation. In Carole A. Goble, Peter P. Chen, and Jia Zhang, editors, 2012 IEEE 19th International Conference on Web Services, Honolulu, HI, USA, June 24–29, 2012, pages 202–209. IEEE Computer Society, 2012.

Mingdong Tang, Wei Liang, Yatao Yang, and Jianguo Xie. A factorization machine-based qos prediction approach for mobile service selection. IEEE Access, 7:32961–32970, 2019.

Mingdong Tang, Tingting Zhang, Jianxun Liu, and Jinjun Chen. Cloud service qos prediction via exploiting collaborative filtering and location-based data smoothing. Concurrency and Computation: Practice and Experience, 27(18):5826–5839, 2015.

Xuna Wang, Qingmei Tan, and Lifan Zhang. A deep neural network of multi-form alliances for personalized recommendations. Information Sciences, 531:68–86, 2020.

Frank Wilcoxon. Individual comparisons by ranking methods. In Breakthroughs in statistics, pages 196–202. Springer, 1992.

Yaoming Wu, Fenfang Xie, Liang Chen, Chuan Chen, and Zibin Zheng. An embedding based factorization machine approach for web service qos prediction. In E. Michael Maximilien, Antonio Vallecillo, Jianmin Wang, and Marc Oriol, editors, Service-Oriented Computing – 15th International Conference, ICSOC 2017, Malaga, Spain, November 13–16, 2017, Proceedings, volume 10601 of Lecture Notes in Computer Science, pages 272–286. Springer, 2017.

Ruibin Xiong, Jian Wang, Zhongqiao Li, Bing Li, and Patrick C. K. Hung. Personalized LSTM based matrix factorization for online qos prediction. In 2018 IEEE International Conference on Web Services, ICWS 2018, San Francisco, CA, USA, July 2–7, 2018, pages 34–41. IEEE, 2018.

Ruibin Xiong, Jian Wang, Neng Zhang, and Yutao Ma. Deep hybrid collaborative filtering for web service recommendation. Expert systems with Applications, 110:191–205, 2018.

Yueshen Xu, Jianwei Yin, ShuiGuang Deng, Neal N. Xiong, and Jianbin Huang. Context-aware qos prediction for web service recommendation and selection. Expert Syst. Appl., 53:75–86, 2016.

Hong-Jian Xue, Xinyu Dai, Jianbing Zhang, Shujian Huang, and Jiajun Chen. Deep matrix factorization models for recommender systems. In Carles Sierra, editor, Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19–25, 2017, pages 3203–3209. ijcai.org, 2017.

Yuyu Yin, Lu Chen, Yueshen Xu, Jian Wan, He Zhang, and Zhida Mai. Qos prediction for service recommendation with deep feature learning in edge computing environment. MONET, 25(2):391–401, 2020.

Y. Yuan, W. Zhang, and X. Zhang. Location-based two-phase clustering for web service qos prediction. In 2016 13th Web Information Systems and Applications Conference (WISA), pages 7–11, 2016.

Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. Deep learning based recommender system: A survey and new perspectives. ACM Comput. Surv., 52(1), February 2019.

Yiwen Zhang, Chunhui Yin, Qilin Wu, Qiang He, and Haibin Zhu. Location-aware deep collaborative filtering for service recommendation. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019.

Zibin Zheng, Hao Ma, Michael R. Lyu, and Irwin King. Qos-aware web service recommendation by collaborative filtering. IEEE Trans. Serv. Comput., 4(2):140–152, 2011.

Jieming Zhu, Pinjia He, Zibin Zheng, and Michael R. Lyu. Online qos prediction for runtime service adaptation via adaptive matrix factorization. IEEE Trans. Parallel Distrib. Syst., 28(10):2911–2924, 2017.

Guobing Zou, Ming Jiang, Sen Niu, Hao Wu, Shengye Pang, and Yanglan Gan. Qos-aware web service recommendation with reinforced collaborative filtering. In Claus Pahl, Maja Vukovic, Jianwei Yin, and Qi Yu, editors, Service-Oriented Computing – 16th International Conference, ICSOC 2018, Hangzhou, China, November 12–15, 2018, Proceedings, volume 11236 of Lecture Notes in Computer Science, pages 430–445. Springer, 2018.

Published

2021-07-08

Issue

Section

Articles