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1.天津大学电气自动化与信息工程学院,天津市 300072
2.国网信息通信产业集团有限公司,北京市 102211
3.国网河北省电力有限公司经济技术研究院,河北省石家庄市 300171
朱介北(1987—),男,博士,教授,博士生导师,主要研究方向:分布式信息能源系统、电力系统稳定分析与控制、新能源控制。E-mail:jiebei.zhu@tju.edu.cn
刘东昊(1998—),男,硕士研究生,主要研究方向:数据中心节能技术。E-mail:liudonghao@tju.edu.cn
李炳森(1975—),男,通信作者,博士研究生,高级工程师,主要研究方向:信息化管理、数字化转型、电网智能化、能源智慧化等。E-mail:libingsen@sgitg.sgcc.com.cn
收稿:2022-09-05,
修回:2023-01-31,
网络出版:2023-06-26,
纸质出版:2023-07-25
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ZHU Jiebei,LIU Donghao,LI Bingsen,et al.Data Center Server Power Consumption Model Based on Temperature Estimation Considering CPU Working State[J].Automation of Electric Power Systems,2023,47(14):140-148.
朱介北,刘东昊,李炳森等.基于CPU工作状态进行温度估算的数据中心服务器功耗模型[J].电力系统自动化,2023,47(14):140-148. DOI: 10.7500/AEPS20220905004.
ZHU Jiebei,LIU Donghao,LI Bingsen,et al.Data Center Server Power Consumption Model Based on Temperature Estimation Considering CPU Working State[J].Automation of Electric Power Systems,2023,47(14):140-148. DOI: 10.7500/AEPS20220905004.
为实现数据中心节能降耗,数据中心服务器功耗模型的精确构建至关重要。传统仅基于CPU利用率的服务器功耗模型,忽略了受温度变量影响的服务器功耗延迟变化动态特性,无法区分在CPU波动或阶跃不同工作状态下的服务器功耗分层现象,导致较大的模型误差。文中提出一种基于CPU工作状态进行温度估算的数据中心服务器功耗模型(PMTC)。PMTC通过在模型建立过程中计及温度变量,捕捉受CPU核心温度延迟变化影响的服务器功耗延迟变化动态特性,消除基于CPU工作状态的服务器功耗分层导致的模型误差;在功耗计算过程中,通过判断CPU工作状态进行CPU核心温度的精确估算,避免了对温度变量的额外测量。通过搭建的功耗测试平台对比了PMTC与传统服务器功耗模型,验证了所提PMTC在不增加额外温度测量设备和输入数据维度的前提下,还原了服务器功耗延迟变化动态特性,显著降低了模型计算误差。
To achieve the energy saving in data centers
it is crucial to accurately construct power consumption models for data center servers. The traditional server power consumption model based solely on CPU utilization ignores the dynamic characteristics of server power delay changes influenced by temperature variables
and cannot distinguish the phenomenon of server power layering under different CPU working states of fluctuation or step
resulting in significant model errors. A data center server power consumption model based on temperature estimation considering CPU working state (PMTC) is proposed. PMTC captures the dynamic characteristics of server power consumption delay changes which are affected by CPU core temperature delay changes considering the temperature variables during the model establishment process
eliminates model errors caused by CPU working states based server power consumption layering. During the power consumption calculation process
accurate estimation of the CPU core temperature is performed by judging the CPU working state
avoiding additional measurement of temperature variables. By comparing the PMTC with traditional server power consumption models through the built power consumption testing platform
it is verified that the proposed PMTC restores the dynamic characteristics of server power consumption delay changes without adding additional temperature measurement devices and inputting data dimensions
significantly reducing model calculation errors
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