Cloud P2P Video Conferencing for Enterprise Networks

In recent years, video conferencing has been a popular internet application in social life, and it also has been an essential tool for enterprise off-site meeting activities. Existed studies are designed for public Internet scenarios, and they assume that the underlying network is fully connected. Participants in video conferencing for large enterprises are spread across a wide geographical area. Some participants are on the intranet, and some are on the Internet outside the intranet. For the sake of network security, communication between the intranet and the internet has to come through certain gateways. Therefore, the underlying network of enterprise video conferencing is not fully connected just as the Internet. For this reason, this paper proposes an Enterprise-Oriented Cloud P2P video conferencing approach, referred to as EOCP. EOCP rents servers in the data centers as relay transmission nodes and utilizes the Peer-to-Peer(P2P) transmission mechanism between these nodes to improve the resource utilization rate. EOCP jointly optimizes P2P organization of the Internet and intranet and the selection of gateways to improve the service quality of video conferencing under cost constraints. Specifically, in EOCP, we build a network resource optimization model for enterprise video conferencing. Furthermore, we design a heuristic algorithm to solve the above model. The experimental results show that EOCP can significantly enhance the quality of service(QoS) in comparison with other solutions. The performance of the designed heuristic algorithm is close to the solution result of the optimal algorithm, and the calculation time is shortened from approximately 2 hours required by the optimal model to around 10 seconds.

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Publication Details

Published
2024-12-13
DOI
https://doi.org/10.1109/iccc62609.2024.10941934
Primary Topic
Peer-to-Peer Network Technologies
Type
article
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article

Cloud P2P Video Conferencing for Enterprise Networks

Jiaxin Li, Yongxiang Zhao
Peer-to-Peer Network Technologies
article

Cloud P2P Video Conferencing for Enterprise Networks

Jiaxin Li, Yongxiang Zhao
article en

Abstract

In recent years, video conferencing has been a popular internet application in social life, and it also has been an essential tool for enterprise off-site meeting activities. Existed studies are designed for public Internet scenarios, and they assume that the underlying network is fully connected. Participants in video conferencing for large enterprises are spread across a wide geographical area. Some participants are on the intranet, and some are on the Internet outside the intranet. For the sake of network security, communication between the intranet and the internet has to come through certain gateways. Therefore, the underlying network of enterprise video conferencing is not fully connected just as the Internet. For this reason, this paper proposes an Enterprise-Oriented Cloud P2P video conferencing approach, referred to as EOCP. EOCP rents servers in the data centers as relay transmission nodes and utilizes the Peer-to-Peer(P2P) transmission mechanism between these nodes to improve the resource utilization rate. EOCP jointly optimizes P2P organization of the Internet and intranet and the selection of gateways to improve the service quality of video conferencing under cost constraints. Specifically, in EOCP, we build a network resource optimization model for enterprise video conferencing. Furthermore, we design a heuristic algorithm to solve the above model. The experimental results show that EOCP can significantly enhance the quality of service(QoS) in comparison with other solutions. The performance of the designed heuristic algorithm is close to the solution result of the optimal algorithm, and the calculation time is shortened from approximately 2 hours required by the optimal model to around 10 seconds.

Beijing Jiaotong University (CN)
Openalex Percentile: Top 66%
Peer-to-Peer Network Technologies
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Cloud P2P Video Conferencing for Enterprise Networks — Jiaxin Li, Yongxiang Zhao · (2024) | TGRS Research Map | TGRS