Dynamic Power Consumption Prediction and Optimization of Data Center by Using Deep Learning and Computational Fluid Dynamics

2018 ◽  
Author(s):  
Hayato Kuwahara ◽  
Ying-Feng Hsu ◽  
Kazuhiro Matsuda ◽  
Morito Matsuoka
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Deep Learning ◽  
Power Consumption ◽  
Data Center ◽  
Dynamic Power ◽  
Power Consumption Prediction ◽  
Consumption Prediction

scholarly journals A Non-Intrusive, Traffic-Aware Prediction Framework for Power Consumption in Data Center Operations

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 663
Author(s):  
Zheng Liu ◽  
Mian Zhang ◽  
Xusheng Zhang ◽  
Yun Li
Keyword(s):  
Power Consumption ◽  
Prediction Model ◽  
Data Center ◽  
Data Centers ◽  
Prediction Performance ◽  
Coarse Grained ◽  
Fine Grained ◽  
Encoding Strategy ◽  
Power Consumption Prediction ◽  
Consumption Prediction

Modern cloud computing relies heavily on data centers, which usually host tens of thousands of servers. Predicting the power consumption accurately in data center operations is crucial for energy optimization. In this paper, we formulate the power consumption prediction at both the fine-grained and coarse-grained level. We carefully discuss the desired properties of an applicable prediction model and propose a non-intrusive, traffic-aware prediction framework for power consumption. We design a character-level encoding strategy for URIs and employ both convolutional and recurrent neural networks to develop a unified prediction model. We use real datasets to simulate requests and analyze the characteristics of the collected power consumption series. Extensive experiments demonstrate that our proposed framework can achieve superior prediction performance compared to other popular leading prediction methods.

Pre-execution power consumption prediction of computational multithreaded workloads

2014 ◽  
Vol 17 (4) ◽  
pp. 1323-1333 ◽  
Author(s):  
Hamid Fadishei ◽  
Hossein Deldari ◽  
Mahmoud Naghibzadeh
Keyword(s):  
Power Consumption ◽  
Multithreaded Workloads ◽  
Power Consumption Prediction ◽  
Consumption Prediction

scholarly journals A new nonlinear lifting line method for aerodynamic analysis and deep learning modeling of small unmanned aerial vehicles

2021 ◽  
Vol 13 ◽  
pp. 175682932110168
Author(s):  
Hasan Karali ◽  
Gokhan Inalhan ◽  
M Umut Demirezen ◽  
M Adil Yukselen
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Deep Learning ◽  
Unmanned Aerial Vehicles ◽  
Aerial Vehicles ◽  
Aerial Vehicle ◽  
Computationally Intensive ◽  
Lifting Line ◽  
Lifting Line Method ◽  
Nonlinear Aerodynamic

In this work, a computationally efficient and high-precision nonlinear aerodynamic configuration analysis method is presented for both design optimization and mathematical modeling of small unmanned aerial vehicles. First, we have developed a novel nonlinear lifting line method which (a) provides very good match for the pre- and post-stall aerodynamic behavior in comparison to experiments and computationally intensive tools, (b) generates these results in order of magnitudes less time in comparison to computationally intensive methods such as computational fluid dynamics. This method is further extended to a complete configuration analysis tool that incorporates the effects of basic fuselage geometries. Moreover, a deep learning based surrogate model is developed using data generated by the new aerodynamic tool that can characterize the nonlinear aerodynamic performance of unmanned aerial vehicles. The major novel feature of this model is that it can predict the aerodynamic properties of unmanned aerial vehicle configurations by using only geometric parameters without the need for any special input data or pre-process phase as needed by other computational aerodynamic analysis tools. The obtained black-box function can calculate the performance of an unmanned aerial vehicle over a wide angle of attack range on the order of milliseconds, whereas computational fluid dynamics solutions take several days/weeks in a similar computational environment. The aerodynamic model predictions show an almost 1-1 coincidence with the numerical data even for configurations with different airfoils that are not used in model training. The developed model provides a highly capable aerodynamic solver for design optimization studies as demonstrated through an illustrative profile design example.

Experimentally Validated Computational Fluid Dynamics Model for Data Center With Active Tiles

2018 ◽  
Vol 140 (1) ◽  
Author(s):  
Jayati Athavale ◽  
Yogendra Joshi ◽  
Minami Yoda
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Flow Rate ◽  
Data Center ◽  
Hot Spot ◽  
Volume Flow Rate ◽  
Optimal Number ◽  
Inlet Temperature ◽  
Cfd Model ◽  
Level Model

Abstract This paper presents an experimentally validated room-level computational fluid dynamics (CFD) model for raised-floor data center configurations employing active tiles. Active tiles are perforated floor tiles with integrated fans, which increase the local volume flow rate by redistributing the cold air supplied by the computer room air conditioning (CRAC) unit to the under-floor plenum. The numerical model of the data center room consists of one cold aisle with 12 racks arranged on both sides and three CRAC units sited around the periphery of the room. The commercial CFD software package futurefacilities6sigmadcx is used to develop the model for three configurations: (a) an aisle populated with ten (i.e., all) passive tiles; (b) a single active tile and nine passive tiles in the cold aisle; and (c) an aisle populated with all active tiles. The predictions from the CFD model are found to be in good agreement with the experimental data, with an average discrepancy between the measured and computed values for total flow rate and rack inlet temperature less than 4% and 1.7 °C, respectively. The validated models were then used to simulate steady-state and transient scenarios following cooling failure. This physics-based and experimentally validated room-level model can be used for temperature and flow distributions prediction and identifying optimal number and locations of active tiles for hot spot mitigation in data centers.

Evaluation of a Data Center Recirculation Non-Uniformity Metric Using Computational Fluid Dynamics

2011 ◽  
Author(s):  
Dustin W. Demetriou ◽  
H. Ezzat Khalifa
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Simple Model ◽  
Temperature Rise ◽  
Data Center ◽  
Large Temperature ◽  
Inlet Temperature ◽  
Temperature Pattern ◽  
Cfd Analysis ◽  
Tile Flow

The work presented in this paper is an extension of the companion work by the authors on a simplified thermodynamic model for data center optimization, in which a recirculation non-uniformity metric, θ, was introduced and used in a parametric analysis to highlight the deleterious effect of recirculation non-uniformity at the inlet of racks on the data center cooling infrastructure power consumption. In this work, several studies are done using a commercial computational fluid dynamics (CFD) package to verify many of the assumptions necessary in the development of the simplified model and to understand the degree of recirculation non-uniformity present in typical data center configurations. A number of CFD simulations are used to quantify the ability of the simple model at predicting θ. The results show that the simple model provides a fairly accurate estimate of θ, with a standard deviation in the prediction error of ∼10–15%. The CFD analysis are also to understand the effect of row length and server temperature rise (ΔTs) temperature non-uniformity. The simulations show that reasonable values of θ range from 2–6 for open aisle data centers depending on operating strategy and data center layout. As a means to understand the effect of buoyancy, a data center Archimedes number (Ar), the ratio of buoyancy to inertia forces, is introduced as a function of tile flow rate and server temperature rise. For servers with modest temperature rise (∼ 10.0°C), Ar is ∼0.1; however, for racks with large temperature rise (∼20°C), Ar > 1.0, meaning buoyancy needs to be considered important. Through CFD analysis the significant effect buoyancy has on the inlet rack temperature patterns is highlighted. The Capture Index (ψ), the ratio of cold air ingested by the racks to the required rack flow, is used to investigate its relationship to the ratio of server flow to tile flow (Y), as the inlet rack temperature patterns are changed by increased Ar. The results show that although the rack inlet temperature patterns are extremely different, ψ does not change significantly as a function of Ar. Lastly, the effect of buoyancy on the assumption of linearity of the temperature field is considered for a range of Ar. The results show the emergence of a stratified temperature pattern at the inlet of the racks as Ar increases and buoyancy becomes more important. It is concluded that under these conditions, a δT change in tile temperature does not produce a δT change in temperature everywhere in the field.

Sign in / Sign up

Export Citation Format

Share Document