Numerical Investigations of Flow Patterns and Thermal Conditions in Large Scale High Compute Density Data Centers

2012 ◽  
Author(s):  
Essam Khalil ◽  
Mena Aziz
Keyword(s):  
Large Scale ◽  
Data Centers ◽  
Thermal Conditions ◽  
Flow Patterns ◽  
Density Data ◽  
Numerical Investigations

On the Computations of Flow Regimes and Thermal Patterns in Large Scale High Compute Density Data Centers

2013 ◽  
Author(s):  
Essam E. Khalil ◽  
Mena H. Aziz
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
High Power ◽  
Data Center ◽  
Power Dissipation ◽  
Large Scale ◽  
Data Centers ◽  
Computer Systems ◽  
Cfd Modelling ◽  
Density Data

High power dissipation from microprocessors, support chips, memory chips and mass storage has resulted in large overall power dissipation from computer systems. The deployment of these computer systems in large numbers and in very dense configurations in a data center had resulted in very high power densities at room level. These computer systems are deployed in a rack. A standard 2-meter high rack can accommodate an equivalent of 34 thin desktop systems. If the maximum power dissipation from each system is 300W, a single rack in a data center can be assumed to dissipate 7.2 KW. A data center can have hundreds of these 7.2 KW racks. Due to such high heat loads, designing the air conditioning system in a data center using simple energy balance is no longer adequate. Moreover, the data center design cannot rely on intuitive design of air distribution. It is necessary to model the airflow and temperature distribution in a data center. In this paper, a computational fluid dynamics model of a prototype data center is presented to make the case for such modeling. The present paper is devoted to investigate the air flow patterns, temperatures and relative humidity in large compute density data centers. Computational fluid dynamics software is utilized to simulate the data center flow pattern. The paper used the simulation techniques as embedded in the commercially available CFD code (FLUENT 6.2). The CFD modelling techniques solved the continuity, momentum and energy conservation equations in addition to standard k–ε model equations for turbulence closure.

On Workload-Aware DRAM Failure Prediction in Large-Scale Data Centers

2021 ◽  
Author(s):  
Xingyi Wang ◽  
Yu Li ◽  
Yiquan Chen ◽  
Shiwen Wang ◽  
Yin Du ◽  
...  
Keyword(s):  
Large Scale ◽  
Data Centers ◽  
Failure Prediction ◽  
Large Scale Data ◽  
Scale Data

DRAM Failure Prediction in Large-Scale Data Centers

2021 ◽  
Author(s):  
Fengyuan Yu ◽  
Hongzuo Xu ◽  
Songlei Jian ◽  
Chenlin Huang ◽  
Yijie Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Data Centers ◽  
Failure Prediction ◽  
Large Scale Data ◽  
Scale Data

scholarly journals Large-scale water balance indicators for different pruning dates of tropical wine grape

2016 ◽  
Vol 51 (7) ◽  
pp. 849-857 ◽  
Author(s):  
Antônio Heriberto de Castro Teixeira ◽  
Jorge Tonietto ◽  
Janice Freitas Leivas
Keyword(s):  
Water Balance ◽  
Large Scale ◽  
Scale Up ◽  
Crop Coefficient ◽  
Thermal Conditions ◽  
Wine Grape ◽  
Wine Quality ◽  
Growing Seasons ◽  
More Care ◽  
Brazilian Northeast

Abstract: The objective of this work was to develop and apply water balance indicators to be scaled up in the wine grape (Vitis vinifera) growing regions of the municipalities of Petrolina and Juazeiro, in the states of Pernambuco and Bahia, respectively, Brazil, simulating different pruning dates along the year. Previous energy balance measurements were used to relate the crop coefficient (Kc) with the accumulated degree-days (DDac). This model was applied to scale up the water balance indicators during the growing seasons. When irrigation water was available, the best pruning periods were from May to July, due to the better natural thermal and hidrological conditions. More care should be taken for pruning done in other periods of the year, regarding the effect of increasing thermal conditions of wine quality. The water balance indicators, both successfully developed and applied, allow large-scale analyses of the thermohydrological conditions for wine grape production under the semiarid conditions of the Brazilian Northeast.

Minimization of Energy Using Heuristic Resource Allocation and Migration for Cloud Computing

2021 ◽  
Vol 12 (1) ◽  
pp. 74-83
Author(s):  
Manjunatha S. ◽  
Suresh L.
Keyword(s):  
Cloud Computing ◽  
Data Center ◽  
Large Scale ◽  
Data Centers ◽  
Service Providers ◽  
Cost Effective ◽  
Cooling Systems ◽  
Operational Costs ◽  
Cloud Computing Service ◽  
And Migration

Data center is a cost-effective infrastructure for storing large volumes of data and hosting large-scale service applications. Cloud computing service providers are rapidly deploying data centers across the world with a huge number of servers and switches. These data centers consume significant amounts of energy, contributing to high operational costs. Thus, optimizing the energy consumption of servers and networks in data centers can reduce operational costs. In a data center, power consumption is mainly due to servers, networking devices, and cooling systems, and an effective energy-saving strategy is to consolidate the computation and communication into a smaller number of servers and network devices and then power off as many unneeded servers and network devices as possible.

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