Purpose

This study aims to present development of genetic algorithm (GA)-based framework aimed at minimizing data center cooling energy consumption by optimizing the cooling set-points while ensuring that thermal management criteria are satisfied.

Design/methodology/approach

Three key components of the developed framework include an artificial neural network-based model for rapid temperature prediction (Athavale et al., 2018a, 2019), a thermodynamic model for cooling energy estimation and GA-based optimization process. The static optimization framework informs the IT load distribution and cooling set-points in the data center room to simultaneously minimize cooling power consumption while maximizing IT load. The dynamic framework aims to minimize cooling power consumption in the data center during operation by determining most energy-efficient set-points for the cooling infrastructure while preventing temperature overshoots.

Findings

Results from static optimization framework indicate that among the three levels (room, rack and row) of IT load distribution granularity, Rack-level distribution consumes the least cooling power. A test case of 7.5 h implementing dynamic optimization demonstrated a reduction in cooling energy consumption between 21%–50% depending on current operation of data center.

Research limitations/implications

The temperature prediction model used being data-driven, is specific to the lab configuration considered in this study and cannot be directly applied to other scenarios. However, the overall framework can be generalized.

Practical implications

The developed framework can be implemented in data centers to optimize operation of cooling infrastructure and reduce energy consumption.

Originality/value

This paper presents a holistic framework for improving energy efficiency of data centers which is of critical value given the high (and increasing) energy consumption by these facilities.

中文翻译：

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基于遗传算法的数据中心冷能优化

目的

本研究旨在介绍基于遗传算法 (GA) 的框架的开发，旨在通过优化冷却设定点同时确保满足热管理标准来最小化数据中心的冷却能耗。

设计/方法/方法

所开发框架的三个关键组件包括用于快速温度预测的人工神经网络模型（Athavale等，2018a，2019）、用于冷却能量估计和基于 GA 的优化过程的热力学模型。静态优化框架通知数据中心机房中的 IT 负载分布和冷却设定点，以同时最小化冷却功耗，同时最大化 IT 负载。动态框架旨在通过确定冷却基础设施的最节能设定点，同时防止温度过冲，最大限度地减少运行期间数据中心的冷却功耗。

发现

静态优化框架的结果表明，在 IT 负载分布粒度的三个级别（房间、机架和行）中，机架级分布消耗的冷却功率最少。实施动态优化的 7.5 小时测试案例表明，根据数据中心的当前运行情况，冷却能耗降低了 21%–50%。

研究限制/影响

所使用的温度预测模型是数据驱动的，特定于本研究中考虑的实验室配置，不能直接应用于其他场景。但是，总体框架可以概括。

实际影响

开发的框架可以在数据中心实施，以优化冷却基础设施的运行并降低能耗。

原创性/价值

本文提出了一个用于提高数据中心能源效率的整体框架，鉴于这些设施的能源消耗很高（并且不断增加），该框架具有关键价值。