A Gray-Box Feedback Control Approach for System-Level Peak Power Management

Jiayu Gong, Chengzhong Xu
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引用次数: 16

Abstract

Power consumption has become one of the most important design considerations for modern high density servers. To avoid system failures caused by power capacity overload or overheating, system-level power management is required. This kind of management needs to control power consumption precisely. Conventional solutions to this problem mostly rely on feedback controllers which only concern the power itself, known as black-box approaches. They may not respond to the variation of system quickly. This paper presents a gray-box strategy to design a model-predictive feedback controller based on a pre-built power model and a performance prediction model to constraint the peak power consumption of a server. In contrast to the existing strategies, this gray-box approach uses the performance events, which bring more insights of the behaviors and power consumption of a system, for the purpose of model prediction. We implemented a prototype of this controller and evaluated it using SPECweb2005 benchmark on a web server. This controller can settle the power consumption below the power cap within 2 control periods for more than 75\% of the power overloading regardless of workload variations, outperforming black-box approaches. Meanwhile, the performance of application can be maximized with this controller.
系统级峰值功率管理的灰盒反馈控制方法
功耗已经成为现代高密度服务器最重要的设计考虑因素之一。为避免电源容量过载或过热导致系统故障,需要对系统级电源进行管理。这种管理需要对电力消耗进行精确的控制。该问题的传统解决方案主要依赖于只关注电源本身的反馈控制器,即所谓的黑盒方法。他们可能不会对系统的变化做出快速反应。提出了一种灰盒策略,在预先建立的功率模型和性能预测模型的基础上设计模型预测反馈控制器,以约束服务器的峰值功耗。与现有的策略相比,这种灰盒方法使用性能事件来进行模型预测,从而更深入地了解系统的行为和功耗。我们实现了该控制器的原型,并在web服务器上使用SPECweb2005基准测试对其进行了评估。该控制器可以在2个控制周期内将功耗控制在功率上限以下,而不受工作负载变化的影响,功率过载的75%以上,优于黑盒方法。同时,该控制器可以最大限度地提高应用程序的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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