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International Journal of Digital Multimedia Broadcasting
Volume 2019, Article ID 1326831, 10 pages
https://doi.org/10.1155/2019/1326831
Research Article

QoS Prediction for Neighbor Selection via Deep Transfer Collaborative Filtering in Video Streaming P2P Networks

1School of Computer and Control Engineering, Yantai University, Yantai 264005, China
2China National Nuclear Corporation, Beijing 100045, China

Correspondence should be addressed to Wenming Ma; moc.621@utymwm

Received 21 September 2018; Accepted 25 October 2018; Published 1 January 2019

Guest Editor: Yuanlong Cao

Copyright © 2019 Wenming Ma et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. B. E. Mada, M. Bagaa, and T. Taleb, “Efficient Transcoding and Streaming Mechanism in Multiple Cloud Domains,” in Proceedings of the 2017 IEEE Global Communications Conference (GLOBECOM 2017), pp. 1–6, Singapore, December 2017. View at Publisher · View at Google Scholar
  2. H. Du, Q. Zheng, W. Zhang, and X. Gao, “A Bandwidth Variation Pattern-Differentiated Rate Adaptation for HTTP Adaptive Streaming over an LTE Cellular Network,” IEEE Access, vol. 6, pp. 9554–9569, 2017. View at Publisher · View at Google Scholar · View at Scopus
  3. J. Li, W. Chen, M. Xiao, F. Shu, and X. Liu, “Efficient Video Pricing and Caching in Heterogeneous Networks,” IEEE Transactions on Vehicular Technology, vol. 65, no. 10, pp. 8744–8751, 2016. View at Publisher · View at Google Scholar · View at Scopus
  4. Y. He, H. Li, X. Cheng, Y. Liu, C. Yang, and L. Sun, “A Blockchain based Truthful Incentive Mechanism for Distributed P2P Applications,” IEEE Access, vol. 6, pp. 27324–27335, 2018. View at Google Scholar · View at Scopus
  5. S.-H. Lin, R. Pal, B.-C. Wang, and L. Golubchik, “On market-driven hybrid-P2P video streaming,” IEEE Transactions on Multimedia, vol. 19, no. 5, pp. 984–998, 2017. View at Publisher · View at Google Scholar · View at Scopus
  6. G. Huang, Y. Gao, L. Kong, and K. Wu, “An incentive scheme based on bitrate adaptation for cloud-assisted P2P video-on-demand streaming systems,” in Proceedings of the 2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), pp. 404–408, Chengdu, April 2018. View at Publisher · View at Google Scholar
  7. J. Junchen, S. Shijie, V. Sekar, and H. Zhang, “Pytheas: Enabling data-driven quality of experience optimization using group based exploration-exploitation,” in Proceedings of the 14th USENIX Symposium on Networked Systems Design and Implementation, pp. 393–406, 2017.
  8. L. Wang, D. Zhang, and H. Yang, “Qos-Awareness variable neighbor selection for mesh-based P2P live streaming system,” in Proceedings of the 2013 IEEE 3rd International Conference on Information Science and Technology, ICIST 2013, pp. 1197–1201, China, March 2013. View at Scopus
  9. A. T. Liem, I.-S. Hwang, A. Nikoukar, C.-Z. Yang, M. S. Ab-Rahman, and C.-H. Lu, “P2P Live-Streaming Application-Aware Architecture for QoS Enhancement in the EPON,” IEEE Systems Journal, vol. 12, no. 1, pp. 648–658, 2018. View at Publisher · View at Google Scholar · View at Scopus
  10. J. Li, Y. Bai, N. Zaman, and V. C. M. Leung, “A Decentralized Trustworthy Context and QoS-Aware Service Discovery Framework for the Internet of Things,” IEEE Access, vol. 5, pp. 19154–19166, 2017. View at Publisher · View at Google Scholar · View at Scopus
  11. K. Wang, H. Yin, W. Quan, and G. Min, “Enabling collaborative edge computing for software defined vehicular networks,” IEEE Network, no. 99, pp. 1–6, 2018. View at Publisher · View at Google Scholar
  12. X. Yang, Y. Guo, Y. Liu, and H. Steck, “A survey of collaborative filtering based social recommender systems,” Computer Communications, vol. 41, pp. 1–10, 2014. View at Publisher · View at Google Scholar · View at Scopus
  13. J. Wei, J. He, K. Chen, Y. Zhou, and Z. Tang, “Collaborative filtering and deep learning based recommendation system for cold start items,” Expert Systems with Applications, vol. 69, pp. 29–39, 2017. View at Google Scholar · View at Scopus
  14. P. Peng, Y. Tian, T. Xiang, Y. Wang, M. Pontil, and T. Huang, “Joint Semantic and Latent Attribute Modelling for Cross-Class Transfer Learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 7, pp. 1625–1638, 2018. View at Google Scholar · View at Scopus
  15. H. Chang, J. Han, C. Zhong, A. M. Snijders, and J.-H. Mao, “Unsupervised Transfer Learning via Multi-Scale Convolutional Sparse Coding for Biomedical Applications,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 5, pp. 1182–1194, 2018. View at Publisher · View at Google Scholar · View at Scopus
  16. Y. Guo, G. Ding, J. Han, and Y. Gao, “Zero-shot learning with transferred samples,” IEEE Transactions on Image Processing, vol. 26, no. 7, pp. 3277–3290, 2017. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  17. G. Wang, W. Li, M. A. Zuluaga et al., “Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning,” IEEE Transactions on Medical Imaging, vol. 37, no. 7, pp. 1562–1573, 2018. View at Google Scholar
  18. X. An, X. Zhou, X. Lü, F. Lin, and L. Yang, “Sample Selected Extreme Learning Machine Based Intrusion Detection in Fog Computing and MEC,” Wireless Communications and Mobile Computing, vol. 2018, Article ID 7472095, 10 pages, 2018. View at Publisher · View at Google Scholar
  19. K. Yanai and Y. Kawano, “Food image recognition using deep convolutional network with pre-training and fine-tuning,” in Proceedings of the 2015 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2015, pp. 1–6, Turin, Italy, July 2015. View at Scopus
  20. M. J. Gangeh, H. Tadayyon, L. Sannachi, A. Sadeghi-Naini, W. T. Tran, and G. J. Czarnota, “Computer Aided Theragnosis Using Quantitative Ultrasound Spectroscopy and Maximum Mean Discrepancy in Locally Advanced Breast Cancer,” IEEE Transactions on Medical Imaging, vol. 35, no. 3, pp. 778–790, 2016. View at Publisher · View at Google Scholar · View at Scopus
  21. H. Yan, Y. Ding, P. Li, Q. Wang, Y. Xu, and W. Zuo, “Mind the class weight bias: Weighted maximum mean discrepancy for unsupervised domain adaptation,” in Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, pp. 945–954, USA, July 2017. View at Scopus
  22. A. Gretton, K. M. Borgwardt, M. J. Rasch et al., “A kernel two-sample test,” Journal of Machine Learning Research (JMLR), vol. 13, pp. 723–773, 2012. View at Google Scholar · View at MathSciNet
  23. Online. 2018. http://www.twitch.tv/.
  24. Online. 2018. http://www.ustream.tv.
  25. Online. 2018. http://www.livestream.com/.
  26. A. O. Al-Abbasi and V. Aggarwal, “EdgeCache: An optimized algorithm for CDN-based over-the-top video streaming services,” in Proceedings of the IEEE INFOCOM 2018 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 202–207, Honolulu, HI, USA, April 2018. View at Publisher · View at Google Scholar
  27. R. Denis, S. Matias, R. Juergen et al., “Service migration from cloud to multi-tier fog nodes for multimedia dissemination with QoE support,” Sensors, vol. 18, no. 2, 2018. View at Google Scholar · View at Scopus
  28. S. Dernbach, N. Taft, J. Kurose, U. Weinsberg, C. Diot, and A. Ashkan, “Cache content-selection policies for streaming video services,” in Proceedings of the 35th Annual IEEE International Conference on Computer Communications, IEEE INFOCOM 2016, USA, April 2016. View at Scopus
  29. X. Wu, B. Cheng, and J. Chen, “Collaborative Filtering Service Recommendation Based on a Novel Similarity Computation Method,” IEEE Transactions on Services Computing, vol. 10, no. 3, pp. 352–365, 2017. View at Publisher · View at Google Scholar · View at Scopus
  30. J. Liu, M. Tang, Z. Zheng, X. Liu, and S. Lyu, “Location-aware and personalized collaborative filtering for web service recommendation,” IEEE Transactions on Services Computing, vol. 10, no. 3, pp. 686–699, 2016. View at Publisher · View at Google Scholar
  31. D. Margaris, C. Vassilakis, and P. Georgiadis, “An integrated framework for adapting WS-BPEL scenario execution using QoS and collaborative filtering techniques,” Science of Computer Programming, vol. 98, pp. 707–734, 2015. View at Publisher · View at Google Scholar · View at Scopus
  32. A. Bellogín, P. Castells, and I. Cantador, “Neighbor Selection and Weighting in User-Based Collaborative Filtering: A Performance Prediction Approach,” ACM Transactions on the Web (TWEB), vol. 8, no. 2, pp. 1–30, 2014. View at Publisher · View at Google Scholar
  33. Z. Jia, Y. Yang, W. Gao, and X. Chen, “User-based collaborative filtering for tourist attraction recommendations,” in Proceedings of the IEEE International Conference on Computational Intelligence and Communication Technology (CICT '15), pp. 22–25, February 2015. View at Publisher · View at Google Scholar · View at Scopus
  34. Z. 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. View at Publisher · View at Google Scholar · View at Scopus
  35. J. Zhu, P. He, Z. Zheng, and M. R. Lyu, “Online QoS Prediction for Runtime Service Adaptation via Adaptive Matrix Factorization,” IEEE Transactions on Parallel and Distributed Systems, vol. 28, no. 10, pp. 2911–2924, 2017. View at Publisher · View at Google Scholar · View at Scopus
  36. R. Zhu, D. Niu, and Z. Li, “Robust web service recommendation via quantile matrix factorization,” in Proceedings of the 2017 IEEE Conference on Computer Communications, INFOCOM 2017, USA, May 2017. View at Scopus
  37. Y. Zhang, Z. Zheng, and M. R. Lyu, “Exploring latent features for memory-based QoS prediction in cloud computing,” in Proceedings of the 30th IEEE International Symposium on Reliable Distributed Systems (SRDS '11), pp. 1–10, IEEE, Madrid, Spain, October 2011. View at Publisher · View at Google Scholar · View at Scopus
  38. D. Yu, Y. Liu, Y. Xu, and Y. Yin, “Personalized QoS prediction for web services using latent factor models,” in Proceedings of the 11th IEEE International Conference on Services Computing, (SCC '14), pp. 107–114, July 2014. View at Publisher · View at Google Scholar · View at Scopus
  39. Y. Koren, “Factorization meets the neighborhood: a multifaceted collaborative filtering model,” in Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '08), pp. 426–434, New York, NY, USA, August 2008. View at Publisher · View at Google Scholar · View at Scopus
  40. W. Lo, J. Yin, S. Deng, Y. Li, and Z. Wu, “An Extended Matrix Factorization Approach for QoS Prediction in Service Selection,” in Proceedings of the 2012 9th IEEE International Conference on Services Computing (SCC), pp. 162–169, Honolulu, HI, USA, June 2012. View at Publisher · View at Google Scholar
  41. R. Salakhutdinov and A. Mnih, “Probabilistic Matrix Factorization,” in Proceedings of the Advances in Neural Information Processing Systems (NIPS’07), pp. 1257–1264, 2007.
  42. D. Daniel, H. Lee, and S. Sebastian, “Algorithms for Non-negative Matrix Factorization,” in Proceedings of the Advances in Neural Information Processing Systems (NIPS’00), pp. 556–562, 2000.
  43. X. Luo, M. Zhou, Y. Xia, and Q. 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. View at Publisher · View at Google Scholar · View at Scopus
  44. A. Hernando, J. Bobadilla, and F. Ortega, “A non negative matrix factorization for collaborative filtering recommender systems based on a Bayesian probabilistic model,” Knowledge-Based Systems, vol. 97, pp. 188–202, 2016. View at Publisher · View at Google Scholar · View at Scopus
  45. H. Shin, H. R. Roth, M. Gao et al., “Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning,” IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1285–1298, 2016. View at Publisher · View at Google Scholar
  46. M. Oquab, L. Bottou, I. Laptev, and J. Sivic, “Learning and transferring mid-level image representations using convolutional neural networks,” in Proceedings of the 27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '14), pp. 1717–1724, IEEE, Columbus, OH, USA, 2015. View at Publisher · View at Google Scholar · View at Scopus
  47. H. Chen, Q. Dou, D. Ni et al., “Automatic fetal ultrasound standard plane detection using knowledge transferred recurrent neural networks,” in Proceedings of the International Conference On Medical Image Computing & Computer Assisted Intervention, pp. 507–514, 2015.
  48. H.-T. Cheng, K. Levent, H. Jeremiah et al., “Wide & Deep Learning for Recommender Systems,” in Proceedings of the1st Workshop on Deep Learning for Recommender Systems, pp. 7–20, 2016.
  49. Y. Shan, T. R. Hoens, J. Jiao, H. Wang, D. Yu, and J. C. Mao, “Deep crossing: Web-scale modeling without manually crafted combinatorial features,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 255–262, USA, August 2016. View at Scopus
  50. X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T. Chua, “Neural Collaborative Filtering,” in Proceedings of the 26th International Conference, pp. 173–182, Perth, Australia, April 2017. View at Publisher · View at Google Scholar