PyStream：增强视频流评估
PyStream: Enhancing Video Streaming Evaluation
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中文总结 AI 辅助
PyStream利用Docker容器网络，实现低成本高精度的本地视频流网络模拟，误差较Wondershaper降低2-3倍，并优于云环境CAdViSE。
中文摘要 AI 辅助
随着流媒体服务变得越来越普遍，在不同网络条件下有效分析其行为至关重要。这通常代价高昂，需要由一台强大的本地机器或云环境模拟具有不同带宽配置的多个播放器。此外，模拟真实的网络行为或确保遵循真实的网络轨迹具有挑战性。本文提出了PyStream，一种简单而强大的视频流网络模拟方式，允许在本地同时运行多个测试。通过利用Docker容器网络，许多实现挑战被抽象化，使所得系统易于管理和升级。我们展示了PyStream不仅降低了对视频流系统测试的要求，而且相对于当前最先进技术提高了模拟的准确性。平均而言，与Wondershaper（许多视频流评估环境中常见的网络流量整形器）相比，PyStream将原始网络轨迹与视频播放器模拟带宽之间的误差减少了2-3倍。此外，与现有的基于云的视频流评估环境（如CAdViSE）相比，PyStream降低了运行实验的成本。
英文摘要
As streaming services become more commonplace, analyzing their behavior effectively under different network conditions is crucial. This is normally quite expensive, requiring multiple players with different bandwidth configurations to be emulated by a powerful local machine or a cloud environment. Furthermore, emulating a realistic network behavior or guaranteeing adherence to a real network trace is challenging. This paper presents PyStream, a simple yet powerful way to emulate a video streaming network, allowing multiple simultaneous tests to run locally. By leveraging a network of Docker containers, many of the implementation challenges are abstracted away, keeping the resulting system easily manageable and upgradeable. We demonstrate how PyStream not only reduces the requirements for testing a video streaming system but also improves the accuracy of the emulations with respect to the current state-of-the-art. On average, PyStream reduces the error between the original network trace and the bandwidth emulated by video players by a factor of 2-3 compared to Wondershaper, a common network traffic shaper in many video streaming evaluation environments. Moreover, PyStream decreases the cost of running experiments compared to existing cloud-based video streaming evaluation environments such as CAdViSE.
发表机构
- Alpen-Adria-Universität Klagenfurt（阿尔卑斯-亚得里亚大学克拉根福分校）
机构由 AI 辅助整理，请以论文原文为准。
