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首页> 外文学位 >Development of artificial neural network based black box model of a data center as a temperature predicting tool as a function of server location, dissipating server heat and crac fan speed.
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Development of artificial neural network based black box model of a data center as a temperature predicting tool as a function of server location, dissipating server heat and crac fan speed.

机译：开发基于人工神经网络的数据中心黑匣子模型作为温度预测工具，该模型是服务器位置，耗散服务器热量和风扇转速的函数。

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摘要

Modern data centers consume an astonishing 1.3% power all around the world. As the number of data centers continue to grow, there is an increasing need and demand to develop new ways to reduce the power footprint. Several approaches are being made to achieve this. One of such several approaches is to develop control systems that would keep the data centers running energy efficiently. Various control theories have been developed throughout the world to achieve the optimal energy efficient state. However, during the synthesis of such control schemes, the CFD simulations take up excessive time for plotting the thermal map of such complex, dynamic and highly nonlinear data center systems. In this paper, we aim to develop and train artificial neural networks for a typical scaled setup of modern data center like a Black Box Model which would predict the temperature at points in the state space throughout the room as a function of the dissipating heat at those points and CRAC fan speeds at the time. Due to significantly low analysis time than computational fluid dynamics, the Black Box is able to predict the temperatures in real time at different points in the setup thereby enabling faster optimization analysis. The Neural model is trained on a huge set of data generated by CFD simulations from hypothetical arrangements in a data center. Discussion about neural network training functions, its training parameters and comparisons of accuracy and computational time and the reason for the same is also done in this paper. Various suggestions to train such highly non linear and dynamic systems are summarized. To prove the accuracy of the neural network, the data generated is compared to the output of the CFD model. The robustness of the Black Box within the training data limits has been verified for changing CRAC fan speed and server heat. The Black Box tool developed is not only accurate but also very fast which enables its use in a feed forward adaptive control setup or the dynamic learning setup or both. Such a Black Box tool mimicking the CFD proves very useful in development of control systems for data centers.
机译：现代数据中心在世界各地消耗的电量惊人地达到1.3％。随着数据中心数量的不断增长，对开发减少功耗的新方法的需求不断增长。正在采取几种方法来实现这一目标。几种方法之一是开发控制系统，以保持数据中心高效运行。全世界已经开发出各种控制理论来实现最佳的节能状态。但是，在这种控制方案的综合过程中，CFD模拟会花费大量时间来绘制此类复杂，动态和高度非线性的数据中心系统的热图。在本文中，我们旨在为现代数据中心的典型规模设置（例如黑匣子模型）开发和训练人工神经网络，该模型可以预测整个房间状态空间中各个点的温度，取决于这些点处的散热量点数和当时的CRAC风扇速度。由于分析时间比计算流体动力学要短得多，因此黑匣子能够实时预测设置中不同点的温度，从而实现更快的优化分析。通过CFD模拟从数据中心中的假设安排生成的大量数据上训练神经模型。本文还讨论了神经网络训练功能，其训练参数以及精度和计算时间的比较及其原因。总结了训练这种高度非线性和动态系统的各种建议。为了证明神经网络的准确性，将生成的数据与CFD模型的输出进行比较。已经验证了黑匣子在训练数据范围内的稳定性，可以更改CRAC风扇速度和服务器热量。开发的黑匣子工具不仅准确而且非常快速，可以在前馈自适应控制设置或动态学习设置或两者中使用。这种模仿CFD的黑匣子工具在数据中心控制系统的开发中非常有用。

著录项

  • 作者

    Date, Chinmay N.;

  • 作者单位

    The University of Texas at Arlington.;

  • 授予单位 The University of Texas at Arlington.;
  • 学科 Engineering Mechanical.
  • 学位 M.S.
  • 年度 2013
  • 页码 80 p.
  • 总页数 80
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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