In 2013, U.S. data centers accounted for 2.2% of the country’s total electricity consumption, a figure that is projected to increase rapidly over the next decade. Many important data center workloads in cloud computing are interactive, and they demand strict levels of quality-of-service (QoS) to meet user expectations, making it challenging to optimize power consumption along with increasing performance demands. This article introduces Hipster, a technique that combines heuristics and reinforcement learning to improve resource efficiency in cloud systems. Hipster explores heterogeneous multi-cores and dynamic voltage and frequency scaling for reducing energy consumption while managing the QoS of the latency-critical workloads. To improve data center utilization and make best usage of the available resources, Hipster can dynamically assign remaining cores to batch workloads without violating the QoS constraints for the latency-critical workloads. We perform experiments using a 64-bit ARM big.LITTLE platform and show that, compared to prior work, Hipster improves the QoS guarantee for Web-Search from 80% to 96%, and for Memcached from 92% to 99%, while reducing the energy consumption by up to 18%. Hipster is also effective in learning and adapting automatically to specific requirements of new incoming workloads just enough to meet the QoS and optimize resource consumption.

中文翻译：

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提高云系统效率的时髦方法

2013 年，美国数据中心占该国总用电量的 2.2%，预计这一数字将在未来十年迅速增长。云计算中的许多重要数据中心工作负载都是交互式的，它们需要严格的服务质量 (QoS) 水平来满足用户的期望，这使得随着性能需求的增加而优化功耗具有挑战性。本文介绍 Hipster，这是一种结合启发式和强化学习来提高云系统资源效率的技术。Hipster 探索了异构多核以及动态电压和频率缩放，以降低能耗，同时管理延迟关键型工作负载的 QoS。为了提高数据中心的利用率并充分利用可用资源，Hipster 可以动态地将剩余内核分配给批处理工作负载，而不会违反延迟关键型工作负载的 QoS 约束。我们使用 64 位 ARM big.LITTLE 平台进行实验，结果表明，与之前的工作相比，Hipster 将 Web-Search 的 QoS 保证从 80% 提高到 96%，将 Memcached 的 QoS 保证从 92% 提高到 99%，同时减少能源消耗高达 18%。Hipster 在自动学习和适应新传入工作负载的特定要求方面也很有效，刚好足以满足 QoS 和优化资源消耗。并将 Memcached 从 92% 提高到 99%，同时将能耗降低多达 18%。Hipster 在自动学习和适应新传入工作负载的特定要求方面也很有效，刚好足以满足 QoS 和优化资源消耗。并将 Memcached 从 92% 提高到 99%，同时将能耗降低多达 18%。Hipster 在自动学习和适应新传入工作负载的特定要求方面也很有效，刚好足以满足 QoS 和优化资源消耗。