Multi-stream 3D video distribution over peer-to-peer networks
The recent advances in stereoscopic video capture, compression, and display have made 3-dimensional (3D) video a visually appealing and costly affordable technology. There have been a series of pioneer works on streaming 3D video over the Internet. Yet the remarkably increased data volume of 3D videos poses great challenges to the conventional client/server design, which has already suffered from supporting 2D videos.
In this paper, we present an initial attempt toward efficient streaming of 3D videos over a peer-to-peer network. We show that the inherent multi-stream nature of 3D video makes playback synchronization more difficult, which is particularly acute with the existence of multiple senders in a peer-to-peer overlay. We address this by a novel 2-stream 2-stage buffer design, together with weighted data scheduling and light-weight synchronization. We further discuss a series of key practical issues toward implementing our peer-to-peer 3D video streaming system, including the weight modeling for data segments, the interactions with the RTP/RTCP protocol stack, and the inter-operability with monoscopic video as well as extension to multi-view video. We have evaluated the performance of our system under different end-system and network configurations with typical 3D video streams. The simulation results demonstrate the superiority of our system in terms of both scalability and streaming quality.
In this paper the authors build up on the progress of open source tools like Hadoop, Hive and Mahout to provide a scalable implementation of quasi-real-time intrusion detection system. The implementation is used to detect Peer-to-Peer Botnet attacks using machine learning approach. The contributions of this paper are as follows: (1) Building a distributed framework using Hive for sniffing and processing network traces enabling extraction of dynamic network features; (2) Using the parallel processing power of Mahout to build Random Forest based Decision Tree model which is applied to the problem of Peer-to-Peer Botnet detection in quasi-real-time. The implementation setup and performance metrics are presented as initial observations and future extensions are proposed.
Multi-stream 3D video distribution over peer-to-peer networks
The recent advances in stereoscopic video capture, compression, and display have made 3-dimensional (3D) video a visually appealing and costly affordable technology. There have been a series of pioneer works on streaming 3D video over the Internet. Yet the remarkably increased data volume of 3D videos poses great challenges to the conventional client/server design, which has already suffered from supporting 2D videos.
In this paper, we present an initial attempt toward efficient streaming of 3D videos over a peer-to-peer network. We show that the inherent multi-stream nature of 3D video makes playback synchronization more difficult, which is particularly acute with the existence of multiple senders in a peer-to-peer overlay. We address this by a novel 2-stream 2-stage buffer design, together with weighted data scheduling and light-weight synchronization. We further discuss a series of key practical issues toward implementing our peer-to-peer 3D video streaming system, including the weight modeling for data segments, the interactions with the RTP/RTCP protocol stack, and the inter-operability with monoscopic video as well as extension to multi-view video. We have evaluated the performance of our system under different end-system and network configurations with typical 3D video streams. The simulation results demonstrate the superiority of our system in terms of both scalability and streaming quality.
In this paper the authors build up on the progress of open source tools like Hadoop, Hive and Mahout to provide a scalable implementation of quasi-real-time intrusion detection system. The implementation is used to detect Peer-to-Peer Botnet attacks using machine learning approach. The contributions of this paper are as follows: (1) Building a distributed framework using Hive for sniffing and processing network traces enabling extraction of dynamic network features; (2) Using the parallel processing power of Mahout to build Random Forest based Decision Tree model which is applied to the problem of Peer-to-Peer Botnet detection in quasi-real-time. The implementation setup and performance metrics are presented as initial observations and future extensions are proposed.
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