华为云采用运维研究为直播服务节省网络带宽成本:GSCO 系统

Xiaoming Yuan, Pengxiang Zhao, Hanyu Hu, Jintao You, Changpeng Yang, Wen Peng, Yonghong Kang, K. M. Teo
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引用次数: 0

摘要

云计算技术的飞速发展引发了各传统行业的模式转变,直播行业就是这一转变的有力例证。自 2020 年以来,华为云的服务已遍及 60 多个国家,成为企业对企业直播领域颇具影响力的企业。在蓬勃发展的流媒体直播市场中,华为云面临着双重挑战:既要满足不断增长的需求,又要管理日益增长的运营成本(主要与网络带宽相关)。为了在提供优质服务的同时最大限度地降低带宽成本,我们开发了一套名为 GSCO 的动态流量分配系统。该系统的设计采用了一系列运筹学方法,如连续优化、整数编程、图论、调度和网络流问题解决,以及最先进的机器学习算法。事实证明,GSCO 系统在成本优化方面非常有效,将网络带宽费用降低了约 30%,从 2020 年第一季度到 2022 年第三季度节省了超过 4960 万美元。此外,该系统还大幅提升了华为云的市场份额,将峰值带宽从最初的每秒 1.5 Tbps 大幅提升至 16 Tbps。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Huawei Cloud Adopts Operations Research for Live Streaming Services to Save Network Bandwidth Cost: The GSCO System
The rapid evolution of cloud computing technologies has instigated a paradigm shift across various traditional industries, with the live streaming sector standing as a compelling exemplification of this transformation. Huawei Cloud, which has become an influential player in the business-to-business live streaming arena, with its services spanning over 60 countries since 2020, is at the forefront of this shift. Amid the flourishing live streaming market, Huawei Cloud faces the dual challenge of satisfying the escalating demand, while managing the mounting operational costs, predominantly associated with the network bandwidth. To offer premium services while minimizing the bandwidth cost, we developed a dynamic traffic allocation system called GSCO. This system was engineered using an array of operations research methodologies such as continuous optimization, integer programming, graph theory, scheduling, and network-flow problem solving, along with state-of-the-art machine learning algorithms. The GSCO system has been proven highly effective in cost optimization, reducing network bandwidth expenses by about 30% and leading to savings exceeding $49.6 million from Q1 2020 to Q3 2022. In addition, it has significantly bolstered Huawei Cloud’s market share, amplifying peak bandwidth from an initial 1.5 terabits per second (Tbps) to a substantial 16 Tbps.
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