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    李振华, 王泓懿, 李洋, 林灏, 杨昕磊. 大规模复杂终端网络的云原生强化设计[J]. 计算机研究与发展, 2024, 61(1): 2-19. DOI: 10.7544/issn1000-1239.202330726
    引用本文: 李振华, 王泓懿, 李洋, 林灏, 杨昕磊. 大规模复杂终端网络的云原生强化设计[J]. 计算机研究与发展, 2024, 61(1): 2-19. DOI: 10.7544/issn1000-1239.202330726
    Li Zhenhua, Wang Hongyi, Li Yang, Lin Hao, Yang Xinlei. Cloud Native Reinforced Design for Large-Scale Complex Terminal Networks[J]. Journal of Computer Research and Development, 2024, 61(1): 2-19. DOI: 10.7544/issn1000-1239.202330726
    Citation: Li Zhenhua, Wang Hongyi, Li Yang, Lin Hao, Yang Xinlei. Cloud Native Reinforced Design for Large-Scale Complex Terminal Networks[J]. Journal of Computer Research and Development, 2024, 61(1): 2-19. DOI: 10.7544/issn1000-1239.202330726

    大规模复杂终端网络的云原生强化设计

    Cloud Native Reinforced Design for Large-Scale Complex Terminal Networks

    • 摘要: 作为互联网数据传输的“最后一公里”,终端网络看似简单却构成99%的性能瓶颈. 经典设计面向典型设备常规环境,难以兼顾多样化场景,导致严重性能落差. 通过云端汇聚并深度诊断大规模终端网络异常,在可用、可靠、可信3个关键维度揭示经典设计多处重要缺陷,采用跨层跨代的协同强化方法针对性修复(如时变非齐次4G/5G双连接管理方法最小化断网概率),实现无场景预设的自调控机制设计. 应用于公安部高速网络、1700万“测网速”app用户、七千万小米手机、一亿百度手机卫士用户以及九亿WiFi设备. 近年来进一步开展基于云端模拟器的前瞻网络设计,无需真实用户设备参与即可发现并修复潜在缺陷,让终端网络设计“生于云、长于云”. 研究成果应用于华为DevEco Studio集成开发环境、腾讯应用市场、谷歌安卓模拟器及字节跳动多款流行应用(如抖音和今日头条).

       

      Abstract: As the “last mile” of Internet content delivery, terminal networks seem rather simple but in fact constitute 99% of the performance bottlenecks. Classic design is usually oriented to typical devices and regular environments, thus making it difficult to accommodate and reproduce diversified scenarios and resulting in severe performance degradation. By comprehensively gathering and deeply diagnosing the anomalies of large-scale complex terminal networks at the cloud, we have revealed several important defects of the classic design for terminal networks in three key dimensions—availability, reliability and credibility. In order to fix these defects effectively and efficiently, the cross-layer and cross-technology collaboratively reinforced design methodology is adopted (e.g., the time-inhomogeneous 4G/5G dual connectivity management method is utilized to minimize the probability of network disconnection), so as to fulfill self-regulation mechanism design for ubiquitous scenarios. The research achievements have been applied to the high-speed network of the Ministry of Public Security, 17 million UUSpeedTest App users, 70 million Xiaomi mobile phones, 100 million Baidu PhoneGuard users, and 900 million WiFi devices. In recent years, we have also conducted forward-looking network design based on cloud-hosted emulators to discover and fix potential defects without real-world user engagement, thus making the design of terminal networks “born in the cloud and grow in the cloud”. The research achievements have been applied to Huawei DevEco Studio IDE (Integrated Development Environment), Tencent App Market, Google Android Emulator, and multiple popular Apps (like Douyin and Toutiao) of ByteDance.

       

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