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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >Resource Management for Power-Constrained HEVC Transcoding Using Reinforcement Learning
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Resource Management for Power-Constrained HEVC Transcoding Using Reinforcement Learning

机译：采用强化学习的功率约束HEVC转码的资源管理

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摘要

The advent of online video streaming applications and services along with the users' demand for high-quality contents require High Efficiency Video Coding (HEVC), which provides higher video quality and more compression at the cost of increased complexity. On one hand, HEVC exposes a set of dynamically tunable parameters to provide trade-offs among Quality-of-Service (QoS), performance, and power consumption of multi-core servers on the video providers' data center. On the other hand, resource management of modern multi-core servers is in charge of adapting system-level parameters, such as operating frequency and multithreading, to deal with concurrent applications and their requirements. Therefore, efficient multi-user HEVC streaming necessitates joint adaptation of application- and system-level parameters. Nonetheless, dealing with such a large and dynamic design space is challenging and difficult to address through conventional resource management strategies. Thus, in this work, we develop a multi-agent Reinforcement Learning framework to jointly adjust application- and system-level parameters at runtime to satisfy the QoS of multi-user HEVC streaming in power-constrained servers. In particular, the design space, composed of all design parameters, is split into smaller independent sub-spaces. Each design sub-space is assigned to a particular agent so that it can explore it faster, yet accurately. The benefits of our approach are revealed in terms of adaptability and quality (with up to to 4x improvements in terms of QoS when compared to a static resource management scheme), and learning time (6 x faster than an equivalent mono-agent implementation). Finally, we show that the power-capping techniques formulated outperform the hardware-based power capping with respect to quality.
机译：在线视频流应用和服务以及用户对高质量内容的需求的出现需要高效的视频编码（HEVC），其提供更高的视频质量和更高的压缩，以增加复杂性。一方面，HEVC公开了一套动态可调参数，以提供视频提供商数据中心的多核服务器的服务质量（QoS），性能和功耗之间的权衡。另一方面，现代多核服务器的资源管理负责适应系统级参数，例如运行频率和多线程，以处理并发应用及其要求。因此，有效的多用户HEVC流必须需要联合适应应用程序和系统级参数。尽管如此，处理如此大型和动态的设计空间挑战，难以通过传统的资源管理策略解决。因此，在这项工作中，我们开发了一个多代理加强学习框架，以在运行时共同调整应用程序和系统级参数，以满足功率受限服务器中的多用户HEVC流的QoS。特别地，由所有设计参数组成的设计空间被分成较小的独立子空间。每个设计子空间被分配给特定代理，以便它可以更快地探索它，但准确。在适应性和质量方面，我们的方法的好处（与静态资源管理方案相比，QoS的高达4倍改进），学习时间（比同等单代理实施比6倍）。最后，我们表明电力覆盖技术制定了越优于基于硬件的功率覆盖的质量。

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