利用知识即服务（KaaS）实现多用户视频转码中的QoS感知资源管理
Leveraging knowledge-as-a-service (KaaS) for QoS-aware resource management in multi-user video transcoding
AI总结：
本文提出利用强化学习构建知识即服务（KaaS）策略，为多用户视频转码中的并发应用自动提供资源与旋钮管理，实验显示对异构QoS请求适应良好，并发用户数提升至1.24倍。
摘要：
共享计算节点中并行应用的共存，每个应用具有不同的服务质量（QoS）要求，在保持可接受的QoS速率的同时，对改善资源占用提出了新的挑战。随着更多应用特定和系统范围的指标被纳入QoS维度，或在资源使用限制严格的情况下，以自动且最优的方式为并发应用构建和提供最合适的动作集（应用控制旋钮和系统资源分配）变得至关重要。在本文中，我们提出利用强化学习技术为并发应用构建和提供此类知识的策略。以多用户视频转码为驱动示例，我们的实验结果表明，资源和旋钮管理对异构QoS请求具有出色的适应性，并且与考虑同构QoS请求的替代方法相比，并发服务的用户数量增加了高达1.24倍。
英文摘要：
The coexistence of parallel applications in shared computing nodes, each one featuring different Quality of Service (QoS) requirements, carries out new challenges to improve resource occupation while keeping acceptable rates in terms of QoS. As more application-specific and system-wide metrics are included as QoS dimensions, or under situations in which resource-usage limits are strict, building and serving the most appropriate set of actions (application control knobs and system resource assignment) to concurrent applications in an automatic and optimal fashion becomes mandatory. In this paper, we propose strategies to build and serve this type of knowledge to concurrent applications by leveraging Reinforcement Learning techniques. Taking multi-user video transcoding as a driving example, our experimental results reveal an excellent adaptation of resource and knob management to heterogeneous QoS requests, and increases in the amount of concurrently served users up to 1.24x compared with alternative approaches considering homogeneous QoS requests.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
