Open AccessPredicting the Session of an P2P IPTV User through Support Vector Regression (SVR)
Author(s) -
Muhammad Ali,
Ihsan Ullah,
Waheed Noor,
Ahthasham Sajid,
Abdul Basit,
Junaid Baber
Publication year - 2020
Publication title -
engineering, technology and applied science research/engineering, technology and applied science research
Language(s) - English
Resource type - Journals
eISSN - 2241-4487
pISSN - 1792-8036
DOI - 10.48084/etasr.3635
Subject(s) - iptv , computer science , session (web analytics) , scalability , computer network , peer to peer , quality of service , identification (biology) , key (lock) , service provider , service (business) , video quality , multimedia , world wide web , computer security , botany , economy , database , economics , biology , metric (unit) , operations management
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.