Abstract
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.
Highlights
The advances in Internet speed and bandwidth over the past decade and the growing acceptance of peer-to-peer (P2P) applications have changed the culture of networking
Since the building blocks in these networks are the peers, they exhibit the behavior of their users and this network becomes very dynamic
The leaving of a peer may disrupt streaming to consumer peers, which is the key problem for Quality of Service (QoS) of the P2P network
Summary
The advances in Internet speed and bandwidth over the past decade and the growing acceptance of peer-to-peer (P2P) applications have changed the culture of networking. An SVR based machine learning model is proposed to predict the session duration of a newly joined peer based on his past data. Such an outcome can be used in a Corresponding author: Ihsan Ullah www.etasr.com. Identification of a stable peer can be helpful to design and maintain stable topology resulting in better delivery of video stream. We compare this model on the same data set with an existing Bayesian network model
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