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Towards Energy Proportionality for Large-Scale Latency-Critical Workloads

机译:对大规模延迟关键工作负载的能量比例

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Reducing the energy footprint of warehouse-scale computer (WSC) systems is key to their affordability, yet difficult to achieve in practice. The lack of energy proportionality of typical WSC hardware and the fact that important workloads (such as search) require all servers to remain up regardless of traffic intensity renders existing power management techniques ineffective at reducing WSC energy use. We present PEGASUS, a feedback-based controller that significantly improves the energy proportionality of WSC systems, as demonstrated by a real implementation in a Google search cluster. PEGASUS uses request latency statistics to dynamically adjust server power management limits in a fine-grain manner, running each server just fast enough to meet global service-level latency objectives. In large cluster experiments, PEGASUS reduces power consumption by up to 20%. We also estimate that a distributed version of PEGASUS can nearly double these savings.
机译:减少仓库规模计算机(WSC)系统的能量足迹是他们的负担能力的关键,但在实践中难以实现。 典型的WSC硬件的能量比例缺乏能量比例和重要的工作负载(例如搜索)要求所有服务器保持保持如何,无论流量强度都在减少WSC能量使用时现有的电源管理技术无效。 我们呈现Pegasus,一种基于反馈的控制器,可显着提高WSC系统的能量比例,如Google搜索群集中的真实实现所示。 Pegasus使用请求延迟统计信息以微粒的方式动态调整服务器电源管理限制,运行每个服务器才能足够快,以满足全球服务级延迟目标。 在大型群集实验中,Pegasus将功耗降低了高达20%。 我们还估计了Pegasus的分布式版本几乎可以逐渐增加这些节省。

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