MAMUT: Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-User Video Transcoding
MAMUT: Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-User Video Transcoding
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MAMUT：多智能体强化学习，实现高效实时多用户视频转码
DOI：
10.23919/date.2019.8715256
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
David Atienza Alonso
中科院分区：
文献类型：
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作者：
Luis Costero;Arman Iranfar;Marina Zapater;Francisco D. Igual;Katzalin Olcoz;David Atienza Alonso
实时视频转码最近被提出作为一种有效的替代方案，以解决当前多用户环境中服务器基础设施中对视频内容不断增长的需求。高效视频编码（HEVC）使高效的在线转码成为可能，因为它通过提供适当的视频配置来增强用户体验，减少网络压力，并最大限度地减少低效和昂贵的视频存储。然而，HEVC的计算复杂度连同其无数配置参数一起对功率管理、吞吐量控制及服务质量（QoS）满意度提出挑战。这在多用户环境中尤其具有挑战性，其中需要同时为具有不同分辨率需求和带宽约束的多个用户提供服务。在这项工作中，我们提出了MAMUT，一种多智能体机器学习方法来应对这些挑战。我们的建议打破了设计空间的转码器和系统参数的运行时适应成更小的子空间，可以探索在一个合理的时间由个别代理。在协作工作时，每个代理负责学习和应用内部HEVC和系统范围参数的最佳值。具体而言，MAMUT动态地调整量化参数，选择每个视频的线程数量，并在压缩和功耗约束下设置具有吞吐量和视频质量目标的工作频率。我们在企业多核服务器上实现MAMUT，并将等效方案与最先进的替代方法进行比较。所获得的结果表明，MAMUT在FPS违规（以及服务质量）方面始终获得高达8倍的改进，功率降低24%，以及更快，更准确地适应视频内容和可用资源。
Real-time video transcoding has recently raised as a valid alternative to address the ever-increasing demand for video contents in servers’ infrastructures in current multi-user environments. High Efficiency Video Coding (HEVC) makes efficient online transcoding feasible as it enhances user experience by providing the adequate video configuration, reduces pressure on the network, and minimizes inefficient and costly video storage. However, the computational complexity of HEVC, together with its myriad of configuration parameters, raises challenges for power management, throughput control, and Quality of Service (QoS) satisfaction. This is particularly challenging in multi-user environments where multiple users with different resolution demands and bandwidth constraints need to be served simultaneously. In this work, we present MAMUT, a multi-agent machine learning approach to tackle these challenges. Our proposal breaks the design space composed of run-time adaptation of the transcoder and system parameters into smaller sub-spaces that can be explored in a reasonable time by individual agents. While working cooperatively, each agent is in charge of learning and applying the optimal values for internal HEVC and system-wide parameters. In particular, MAMUT dynamically tunes Quantization Parameter, selects number of threads per video, and sets the operating frequency with throughput and video quality objectives under compression and power consumption constraints. We implement MAMUT on an enterprise multicore server and compare equivalent scenarios to state-of-the-art alternative approaches. The obtained results reveal that MAMUT consistently attains up to 8x improvement in terms of FPS violations (and thus Quality of Service), 24% power reduction, as well as faster and more accurate adaptation both to the video contents and available resources.