Predicting the Session of an P2P IPTV User through Support Vector Regression (SVR)
Scalability and ease of implementation make Peer-to-Peer (P2P) infrastructure an attractive option for live video streaming. Peer end-users or peers in these networks have extremely complex features and exhibit unpredictable behavior, i.e. any peer may join or exit the network without prior notice. Peers' dynamics is considered one of the key problems impacting the Quality of Service (QoS) of the P2P based IPTV services. Since, peer dynamics results in video disruption to consumer peers, for smooth video distribution, stable peer identification and selection is essential. Many research works have been conducted on stable peer identification using classical statistical methods. In this paper, a model based on machine learning is proposed in order to predict the length of a user session on entering the network. This prediction can be utilized in topology management such as offloading the departing peer before its exit. Consequently, this will help peers to select stable provider peers, which are the ones with longer session duration. Furthermore, it will also enable service providers to identify stable peers in a live video streaming network. Results indicate that the SVR based model performance is superior to an existing Bayesian network model.
Keywords:P2P IPTV, user behavior, machine learning, SVR, Bayesian network, session prediction
Y. Tang, L. Sun, J.-G. Luo, S.-Q. Yang, and Y. Zhong, "Improving Quality of Live Streaming Service over P2P Networks with User Behavior Model," in Advances in Multimedia Modeling, T.-J. Cham, J. Cai, C. Dorai, D. Rajan, T.-S. Chua, and L.-T. Chia, Eds. Berlin, Heidelberg: Springer, 2006, pp. 333-342. DOI: https://doi.org/10.1007/978-3-540-69429-8_34
F. Wang, Y. Xiong, and J. Liu, "mTreebone: A Collaborative Tree-Mesh Overlay Network for Multicast Video Streaming," IEEE Transactions on Parallel and Distributed Systems, vol. 21, no. 3, pp. 379-392, Mar. 2010. DOI: https://doi.org/10.1109/TPDS.2009.77
I. Ullah, G. Doyen, G. Bonnet, and D. Gaïti, "A Bayesian approach for user aware peer-to-peer video streaming systems," Signal Processing: Image Communication, vol. 27, no. 5, pp. 438-456, May 2012. DOI: https://doi.org/10.1016/j.image.2012.02.007
B. Zhang et al., "Understanding user behavior in Spotify," Proceedings of the IEEE INFOCOM 2013 (Turin, Italy, April 14-19, 2013), pp. 220-224, 2013. DOI: https://doi.org/10.1109/INFCOM.2013.6566767
S. Budhkar and V. Tamarapalli, "An overlay management strategy to improve peer stability in P2P live streaming systems," in 2016 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), Nov. 2016, pp. 1-6 DOI: https://doi.org/10.1109/ANTS.2016.7947813
K. Park, D. Chang, J. Kim, W. Yoon, and T. Kwon, "An Analysis of User Dynamics in P2P Live Streaming Services," in 2010 IEEE International Conference on Communications, Cape Town, South Africa, May 2010. DOI: https://doi.org/10.1109/ICC.2010.5502009
Liu, Z., Wu, C., Li, B., Zhao, S., 2009. Distilling Superior Peers in Large-Scale P2P Streaming Systems, in: IEEE INFOCOM 2009. Presented at the IEEE INFOCOM 2009, IEEE, Rio de Janeiro, Brazil, pp. 82-90. DOI: https://doi.org/10.1109/INFCOM.2009.5061909
I. Ullah, G. Doyen, G. Bonnet, and D. Gaiti, "A Survey and Synthesis of User Behavior Measurements in P2P Streaming Systems," IEEE Communications Surveys Tutorials, vol. 14, no. 3, pp. 734-749, Third 2012 DOI: https://doi.org/10.1109/SURV.2011.082611.00134
A. Lekharu, K. Y. Moulii, A. Sur, and A. Sarkar, "Deep Learning based Prediction Model for Adaptive Video Streaming," in 2020 International Conference on COMmunication Systems NETworkS (COMSNETS), Jan. 2020, pp. 152-159. DOI: https://doi.org/10.1109/COMSNETS48256.2020.9027383
W. Ma, Q. Zhang, C. Mu, and M. Zhang, "QoS Prediction for Neighbor Selection via Deep Transfer Collaborative Filtering in Video Streaming P2P Networks," International Journal of Digital Multimedia Broadcasting, vol. 2019, Jan. 2019, Art. no. 1326831. DOI: https://doi.org/10.1155/2019/1326831
M. Sina, M. Dehghan, and A. M. Rahmani, "CaR-PLive: Cloud-assisted reinforcement learning based P2P live video streaming: a hybrid approach," Multimedia Tools and Applications, vol. 78, no. 23, pp. 34095-34127, Dec. 2019. DOI: https://doi.org/10.1007/s11042-019-08102-1
K. Pal, M. C. Govil, and M. Ahmed, "FLHyO: fuzzy logic based hybrid overlay for P2P live video streaming," Multimedia Tools and Applications, vol. 78, no. 23, pp. 33679-33702, Dec. 2019. DOI: https://doi.org/10.1007/s11042-019-08010-4
A. Ghaderzadeh, M. Kargahi, and M. Reshadi, "ReDePoly: reducing delays in multi-channel P2P live streaming systems using distributed intelligence," Telecommunication Systems, vol. 67, no. 2, pp. 231-246, Feb. 2018. DOI: https://doi.org/10.1007/s11235-017-0336-x
I. Basicevic, D. Kukolj, S. Ocovaj, G. Cmiljanovic, and N. Fimic, "A Fast Channel Change Technique Based on Channel Prediction," IEEE Transactions on Consumer Electronics, vol. 64, no. 4, pp. 418-423, Nov. 2018. DOI: https://doi.org/10.1109/TCE.2018.2875271
T. Vasiloudis, H. Vahabi, R. Kravitz, and V. Rashkov, "Predicting Session Length in Media Streaming," presented at the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017, Tokyo, Japan, Aug. 2017, pp. 977-980. DOI: https://doi.org/10.1145/3077136.3080695
M. Awad and R. Khanna, "Support Vector Regression," in Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers, M. Awad and R. Khanna, Eds. Berkeley, CA: Apress, 2015, pp. 67-80. DOI: https://doi.org/10.1007/978-1-4302-5990-9_4
F. Pedregosa et al., "Scikit-learn: Machine Learning in Python," Journal of Machine Learning Research, vol. 12, no. 85, pp. 2825-2830, 2011.
G. Chen and Z. Ge, "Robust Bayesian networks for low-quality data modeling and process monitoring applications," Control Engineering Practice, vol. 97, Apr. 2020, Art no. 104344. DOI: https://doi.org/10.1016/j.conengprac.2020.104344
T. Talvitie, R. Eggeling, and M. Koivisto, "Learning Bayesian networks with local structure, mixed variables, and exact algorithms," International Journal of Approximate Reasoning, vol. 115, pp. 69-95, Dec. 2019. DOI: https://doi.org/10.1016/j.ijar.2019.09.002
L. Azzimonti, G. Corani, and M. Zaffalon, "Hierarchical estimation of parameters in Bayesian networks," Computational Statistics & Data Analysis, vol. 137, pp. 67-91, Sep. 2019. DOI: https://doi.org/10.1016/j.csda.2019.02.004
G. Bonnet, I. Ullah, G. Doyen, L. Fillatre, D. Gaïti, and I. Nikiforov, "A Semi-Markovian Individual Model of Users for P2P Video Streaming Applications," in 2011 4th IFIP International Conference on New Technologies, Mobility and Security, Paris, France, Feb. 2011. DOI: https://doi.org/10.1109/NTMS.2011.5721042
A. Tanovic, I. Androulidakis, and F. Orucevic, "Analysis of IPTV Channels Watching Preferences in Bosnia and Herzegovina," Engineering, Technology & Applied Science Research, vol. 1, no. 5, pp. 105-113, Oct. 2011. DOI: https://doi.org/10.48084/etasr.78
N. T. Dung and N. T. Phuong, "Short-Term Electric Load Forecasting Using Standardized Load Profile (SLP) And Support Vector Regression (SVR)," Engineering, Technology & Applied Science Research, vol. 9, no. 4, pp. 4548-4553, Aug. 2019. DOI: https://doi.org/10.48084/etasr.2929
"Google Code Archive - Long-term storage for Google Code Project Hosting." https://code.google.com/archive/p/bnt/ (accessed Jul. 01, 2020).
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