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.

翻译：共享计算节点上并行应用的共存，每个应用具有不同的服务质量（QoS）要求，在改善资源占用的同时保持可接受的QoS水平带来了新挑战。随着更多特定应用和系统级指标被纳入QoS维度，或在资源使用限制严格的情况下，以自动化和最优方式为并发应用构建并提供最合适的动作集合（应用控制旋钮和系统资源分配）变得至关重要。本文利用强化学习技术提出构建并提供此类知识给并发应用的策略。以多用户视频转码为驱动示例，我们的实验结果表明，资源与旋钮管理对异构QoS请求具有出色的适应性，与考虑同构QoS请求的替代方法相比，并发服务的用户数量最多提升至1.24倍。