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Title: Optimization of Workload Distribution of Data Centers Based on a Self-Learning In Situ Adaptive Tabulation Model

Abstract

Data center cooling typically involves non-uniform airflow and temperature distributions, which are affected by the IT workload distribution. It is helpful to simulate the airflow and temperature to optimize the workload distribution. Traditional computational fluid dynamics (CFD) simulation is usually time-consuming while conventional reduced order models (ROMs), though computationally fast, may generate inaccurate results even after being fully trained. In Situ Adaptive Tabulation (ISAT), contracting to conventional ROM, can make prediction with error lower than a user-specified tolerance. To demonstrate using of ISAT for optimal workload distribution in data center, this paper presents a preliminary study of an ISAT-based genetic algorithm optimization platform. The ISAT is trained offline by using the results from CFD simulations using a hypothetical simple data center. The optimal workload distribution determined by the platform leads to approximately 6.8% of energy savings when compared to the benchmark with a uniform workload distribution. We note that the time cost for the entire optimization process, including the training of ISAT is about 4 hours, which is acceptable in the design phase.

Authors:
; ; ;
Publication Date:
Research Org.:
University of Colorado Boulder
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)
OSTI Identifier:
1571179
DOE Contract Number:  
EE0007688
Resource Type:
Conference
Resource Relation:
Conference: the 16th Conference of International Building Performance Simulation Association (Building Simulation 2019), September 2-4, Rome, Italy
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; energy simulation; data center; ISAT

Citation Formats

Han, Xu, Tian, Wei, Zuo, Wangda, and VanGilder, James W. Optimization of Workload Distribution of Data Centers Based on a Self-Learning In Situ Adaptive Tabulation Model. United States: N. p., 2019. Web.
Han, Xu, Tian, Wei, Zuo, Wangda, & VanGilder, James W. Optimization of Workload Distribution of Data Centers Based on a Self-Learning In Situ Adaptive Tabulation Model. United States.
Han, Xu, Tian, Wei, Zuo, Wangda, and VanGilder, James W. Mon . "Optimization of Workload Distribution of Data Centers Based on a Self-Learning In Situ Adaptive Tabulation Model". United States. https://www.osti.gov/servlets/purl/1571179.
@article{osti_1571179,
title = {Optimization of Workload Distribution of Data Centers Based on a Self-Learning In Situ Adaptive Tabulation Model},
author = {Han, Xu and Tian, Wei and Zuo, Wangda and VanGilder, James W.},
abstractNote = {Data center cooling typically involves non-uniform airflow and temperature distributions, which are affected by the IT workload distribution. It is helpful to simulate the airflow and temperature to optimize the workload distribution. Traditional computational fluid dynamics (CFD) simulation is usually time-consuming while conventional reduced order models (ROMs), though computationally fast, may generate inaccurate results even after being fully trained. In Situ Adaptive Tabulation (ISAT), contracting to conventional ROM, can make prediction with error lower than a user-specified tolerance. To demonstrate using of ISAT for optimal workload distribution in data center, this paper presents a preliminary study of an ISAT-based genetic algorithm optimization platform. The ISAT is trained offline by using the results from CFD simulations using a hypothetical simple data center. The optimal workload distribution determined by the platform leads to approximately 6.8% of energy savings when compared to the benchmark with a uniform workload distribution. We note that the time cost for the entire optimization process, including the training of ISAT is about 4 hours, which is acceptable in the design phase.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {9}
}

Conference:
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