End users stream video increasingly from live broadcasters (via YouTube Live, Twitch etc.). Adaptive live video streaming is realised by transcoding different representations of the original video content. Management of transcoding resources creates costs for the service provider, because transcoding is a CPU-intensive task. Additionally, the content must be transcoded within real time with the transcoding resources in order to provide satisfying Quality of Service. The contribution of this paper is validation of an online architecture for enabling live video transcoding with Docker in a Kubernetes-based cloud environment. Particularly, online cloud resource allocation has been focused on by executing experiments in several configurations. The results indicate that Random Forest regressor provided the best overall performance in terms of precision regarding transcoding speed and CPU consumption on resources, and the amount of realised transcoding tasks. Reinforcement Learning provided lower performance, and required more effort in terms of training.

中文翻译：

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在线架构，用于预测实时视频转码资源

最终用户越来越多地从直播台流式传输视频（通过YouTube Live，Twitch等）。自适应直播视频流是通过对原始视频内容的不同表示进行转码来实现的。转码资源的管理为服务提供商带来了成本，因为转码是一项占用大量CPU的任务。此外，必须使用转码资源实时对内容进行转码，以提供令人满意的服务质量。本文的贡献在于验证了一种在线架构，该架构可在基于Kubernetes的云环境中使用Docker进行实时视频转码。特别地，在线云资源分配已经通过在几种配置中执行实验而集中于。结果表明，随机森林回归器在代码转换速度和资源上的CPU消耗以及已实现的代码转换任务的数量方面的精度方面提供了最佳的整体性能。强化学习的绩效较低，在培训方面需要更多的精力。