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Computer Science > Networking and Internet Architecture

arXiv:2401.15839 (cs)
[Submitted on 29 Jan 2024]

Title:Swarm: Cost-Efficient Video Content Distribution with a Peer-to-Peer System

Authors:Dehui Wei, Jiao Zhang, Haozhe Li, Zhichen Xue, Yajie Peng, Xiaofei Pang, Rui Han, Yan Ma, Jialin Li
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Abstract:As ByteDance's business expands, the substantial infrastructure expenses associated with centralized Content Delivery Network (CDN) networks have rendered content distribution costs prohibitively high. In response, we embarked on exploring a peer-to-peer (P2P) network as a promising solution to alleviate the escalating costs of content distribution. However, the decentralized nature of P2P often introduces performance challenges, given the diversity and dispersion of peer devices. This study introduces Swarm, ByteDance's innovative hybrid system for video streaming. Swarm seamlessly integrates the robustness of a conventional CDN with the cost-efficiency of a decentralized P2P network. Its primary aim is to provide users with reliable streaming quality while minimizing traffic expenses. To achieve this, Swarm employs a centralized control plane comprised of a tracker cluster, overseeing a data plane with numerous edge residual resources. The tracker also takes on the responsibility of mapping clients to servers. Addressing the performance disparities among individual peer servers, Swarm utilizes our proprietary multipath parallel transmission method for communication between clients and peer servers. Operating stably for six years, Swarm now manages over a hundred thousand peer servers, serving nearly a hundred million users daily and saving the company hundreds of millions of RMB annually. Experimental results affirm that, while significantly cutting costs, Swarm performs on par with traditional CDNs.
Subjects: Networking and Internet Architecture (cs.NI)
Cite as: arXiv:2401.15839 [cs.NI]
  (or arXiv:2401.15839v1 [cs.NI] for this version)
  https://doi.org/10.48550/arXiv.2401.15839

Submission history

From: Dehui Wei [view email]
[v1] Mon, 29 Jan 2024 02:19:42 UTC (2,425 KB)
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