USENIX ATC2024顶会
Enhancing Resource Management of the World's Largest PCDN System for On-Demand Video Streaming
Ruixiao Zhang, Haiping Wang, Shu Shi, Xiaofei Pang, Yajie Peng, Zhichen Xue, Jiangchuan Liu
摘要
The rapid growth of video services has led to the significant requirement for efficient content delivery. Traditional approaches mainly rely on Content Delivery Networks (CDNs), which unfortunately incur significant bandwidth cost for video providers. To resolve this problem, the cost-efficient edge resources have emerged as a new solution to replace CDNs. However, their heterogeneous hardware and poor performance still present challenges in their effective utilization. In this paper, we present how ByteDance explores the use of these cost-efficient but less performant resources. Specifically, we first present an extensive overview of PCDN, ByteDance's alternative delivery network for CDNs. Second, as PCDN encounters significant resource imbalances after years of deployment, we further introduce PCDN + , the enhanced iteration of PCDN. Specifically, by integrating a well-designed centralized/decentralized framework, we evolve previous "static" and "uncontrolled" PCDN into a "dynamic" and "controlled" system. The extensive A/B test and real-world deployment have demonstrated that PCDN + 1) effectively alleviates overloading issues, 2) significantly improves the utilization of low-cost resources, 3) provides higher service speed.
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