在线数据密集型业务的电源管理

David Meisner, Christopher M. Sadler, L. Barroso, W. Weber, T. Wenisch
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引用次数: 423

摘要

Internet服务模型的成功在很大程度上可以归因于一类工作负载的流行,我们称之为在线数据密集型(OLDI)服务。这些工作负载在每个用户请求上对大量数据集执行重要的计算,但是,与它们的离线对应(例如MapReduce计算)不同,它们需要在高请求率下在亚秒级时间尺度内进行响应。大型搜索产品、在线广告和机器翻译是此类工作负载的示例。尽管OLDI服务中的负载在一天中变化很大,但由于底层机器缺乏能量比例性,它们的能耗变化很小。OLDI工作负载的规模和延迟敏感性也使它们成为电源管理技术的一个具有挑战性的目标。我们将研究如何(如果有的话)使OLDI系统更符合能量比例。具体来说,我们评估了活动和空闲低功耗模式的适用性,以减少主要服务器组件(处理器、内存和磁盘)消耗的功率,同时保持严格的响应时间限制,特别是在95百分位延迟上。使用Web搜索作为此工作负载类的代表性示例,我们首先描述集群范围内的生产Web搜索工作负载。我们提供了精细的特性描述,并揭示了使用每个主服务器组件的低功耗模式来节省电力的机会。其次,我们开发并验证了一个性能模型,以评估基于处理器和内存的低功耗模式对搜索延迟分布的影响,并考虑当前和可预见的低功耗模式的好处。我们的结果突出了这类工作负载的电源管理面临的挑战。与其他服务器工作负载相比,空闲低功耗模式显示出巨大的前景,对于OLDI工作负载,我们发现只有使用协调的、全系统活动的低功耗模式才能实现具有可接受查询延迟的能量比例。
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Power management of online data-intensive services
Much of the success of the Internet services model can be attributed to the popularity of a class of workloads that we call Online Data-Intensive (OLDI) services. These work-loads perform significant computing over massive data sets per user request but, unlike their offline counterparts (such as MapReduce computations), they require responsiveness in the sub-second time scale at high request rates. Large search products, online advertising, and machine translation are examples of workloads in this class. Although the load in OLDI services can vary widely during the day, their energy consumption sees little variance due to the lack of energy proportionality of the underlying machinery. The scale and latency sensitivity of OLDI workloads also make them a challenging target for power management techniques. We investigate what, if anything, can be done to make OLDI systems more energy-proportional. Specifically, we evaluate the applicability of active and idle low-power modes to reduce the power consumed by the primary server components (processor, memory, and disk), while maintaining tight response time constraints, particularly on 95th-percentile latency. Using Web search as a representative example of this workload class, we first characterize a production Web search workload at cluster-wide scale. We provide a fine-grain characterization and expose the opportunity for power savings using low-power modes of each primary server component. Second, we develop and validate a performance model to evaluate the impact of processor- and memory-based low-power modes on the search latency distribution and consider the benefit of current and foreseeable low-power modes. Our results highlight the challenges of power management for this class of workloads. In contrast to other server workloads, for which idle low-power modes have shown great promise, for OLDI workloads we find that energy-proportionality with acceptable query latency can only be achieved using coordinated, full-system active low-power modes.
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