Scalable Video Conferencing Using SDN Principles
March 14, 2025 Β· Declared Dead Β· π Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Oliver Michel, Satadal Sengupta, Hyojoon Kim, Ravi Netravali, Jennifer Rexford
arXiv ID
2503.11649
Category
cs.NI: Networking & Internet
Citations
1
Venue
Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication
Last Checked
4 months ago
Abstract
Video-conferencing applications face an unwavering surge in traffic, stressing their underlying infrastructure in unprecedented ways. This paper rethinks the key building block for conferencing infrastructures -- selective forwarding units (SFUs). SFUs relay and adapt media streams between participants and, today, run in software on general-purpose servers. Our main insight, discerned from dissecting the operation of production SFU servers, is that SFUs largely mimic traditional packet-processing operations such as dropping and forwarding. Guided by this, we present Scallop, an SDN-inspired SFU that decouples video-conferencing applications into a hardware-based data plane for latency-sensitive and frequent media operations, and a software control plane for the (infrequent) remaining tasks, such as analyzing feedback signals. Our Tofino-based implementation fully supports WebRTC and delivers 7-210 times improved scaling over a 32-core commodity server, while reaping performance improvements by cutting forwarding-induced latency by 26 times.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Networking & Internet
R.I.P.
π»
Ghosted
π
π
The Cartographer
Federated Learning in Mobile Edge Networks: A Comprehensive Survey
π
π
The Cartographer
A Survey of Indoor Localization Systems and Technologies
R.I.P.
π»
Ghosted
Survey of Important Issues in UAV Communication Networks
π
π
The Cartographer
Network Function Virtualization: State-of-the-art and Research Challenges
π
π
The Cartographer
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted