scholarly journals Comparative Study of Energy Performance between Chip and Inlet Temperature-Aware Workload Allocation in Air-Cooled Data Center

Energies ◽  
2018 ◽  
Vol 11 (3) ◽  
pp. 669 ◽  
Author(s):  
◽  
◽  
Keyword(s):  
Comparative Study ◽  
Data Center ◽  
Energy Performance ◽  
Inlet Temperature ◽  
Workload Allocation

Power-aware workload allocation for green data centers

2018 ◽  
Vol 29 (4) ◽  
pp. 678-703
Author(s):  
Louma Ahmad Chaddad ◽  
Ali Chehab ◽  
Imad Elhajj ◽  
Ayman Kayssi
Keyword(s):  
Performance Optimization ◽  
Data Center ◽  
Data Centers ◽  
Energy Savings ◽  
Thermal State ◽  
Energy Performance ◽  
Content Type ◽  
Non Linear Programming ◽  
Server Selection ◽  
Workload Allocation

Purpose The purpose of this paper is to present an approach to reduce energy consumption in data centers. Subsequently, it reduces electricity bills and carbon dioxide footprints resulting from their use. Design/methodology/approach The authors present a mathematical model of the energy dissipation optimization problem. The authors formulate analytically the server selection problem and the supply air temperature as a non-linear programming, and propose an algorithm to solve it dynamically. Findings A simulation study on SimWare, using real workload traces, shows considerable savings for different data center sizes and utilization rates as compared to three other classic algorithms. The results prove that the proposed algorithm is efficient in handling the energy-performance trade-off, and that the proposed algorithm provides significant energy savings and maintains a relatively homogenous and stable thermal state at the different rack units in the data center. Originality/value The proposed algorithm ensures energy provisioning, performance optimization over existing state-of-the-art heuristics, and on-demand workload allocation.

A comparative study on energy performance assessment for HVAC systems in high-tech fabs

2021 ◽  
Vol 39 ◽  
pp. 102188
Author(s):  
Yu Zhao ◽  
Nan Li ◽  
Chenyang Tao ◽  
Qiong Chen ◽  
Mengqi Jiang
Keyword(s):  
Comparative Study ◽  
Performance Assessment ◽  
Energy Performance ◽  
Hvac Systems ◽  
High Tech

scholarly journals Thermal Performance and Efficiency of a Mineral Oil Immersed Server Over Varied Environmental Operating Conditions

2017 ◽  
Vol 139 (4) ◽  
Author(s):  
Richard Eiland ◽  
John Edward Fernandes ◽  
Marianna Vallejo ◽  
Ashwin Siddarth ◽  
Dereje Agonafer ◽  
...  
Keyword(s):  
Flow Rate ◽  
Thermal Performance ◽  
Data Center ◽  
Mineral Oil ◽  
Operating Conditions ◽  
Published Data ◽  
Inlet Temperature ◽  
Air Cooling ◽  
Bulk Fluid ◽  
Oil Immersion

Complete immersion of servers in dielectric mineral oil has recently become a promising technique for minimizing cooling energy consumption in data centers. However, a lack of sufficient published data and long-term documentation of oil immersion cooling performance make most data center operators hesitant to apply these approaches to their mission critical facilities. In this study, a single server was fully submerged horizontally in mineral oil. Experiments were conducted to observe the effects of varying the volumetric flow rate and oil inlet temperature on thermal performance and power consumption of the server. Specifically, temperature measurements of the central processing units (CPUs), motherboard (MB) components, and bulk fluid were recorded at steady-state conditions. These results provide an initial bounding envelope of environmental conditions suitable for an oil immersion data center. Comparing with results from baseline tests performed with traditional air cooling, the technology shows a 34.4% reduction in the thermal resistance of the system. Overall, the cooling loop was able to achieve partial power usage effectiveness (pPUECooling) values as low as 1.03. This server level study provides a preview of possible facility energy savings by utilizing high temperature, low flow rate oil for cooling. A discussion on additional opportunities for optimization of information technology (IT) hardware and implementation of oil cooling is also included.

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.

scholarly journals Thermal Turbulent Flow in Leading Edge Grooved and Conventional Tilting Pad Journal Bearing Segments—A Comparative Study

Lubricants ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 97 ◽  
Author(s):  
Philip Croné ◽  
Andreas Almqvist ◽  
Roland Larsson
Keyword(s):  
Comparative Study ◽  
Journal Bearing ◽  
Flow Characteristics ◽  
Leading Edge ◽  
Inlet Temperature ◽  
Average Value ◽  
Tilting Pad ◽  
Sst Turbulence Model ◽  
Effective Boundary Condition ◽  
Tilting Pad Journal Bearing

A comparative study between a conventional- and leading edge grooved (LEG) tilting pad journal bearing (TPJB) segment is performed. The developed model uses the Shear Stress Transport (SST) turbulence model, coupled with the energy equation and a partial differential equation for the fluid domain mesh displacement to predict the thermal flow characteristics. Instead of using an effective boundary condition to determine the inlet temperature of the LEG pad and excluding the additional LEG portion, as is common practice, the whole geometry of the LEG is modeled. Several sizes of the LEG portion is investigated and it is shown to have quite a small influence on pressure, temperature, film thickness and turbulence intensity. Moreover, the results also show that the conventional pad gives rise to higher levels of turbulence in the mid plane compared to its LEG counterpart, while the latter has a marginally higher value of turbulence when the volume average value is computed. The maximum value of turbulence is however present in the conventional model.

Spatially-Aware Optimization of Energy Consumption in Consolidated Data Center Systems

2011 ◽  
Author(s):  
Hui Chen ◽  
Mukil Kesavan ◽  
Karsten Schwan ◽  
Ada Gavrilovska ◽  
Pramod Kumar ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Data Center ◽  
Power Distribution ◽  
Total Power ◽  
Inlet Temperature ◽  
Workload Management ◽  
Thermal Distribution ◽  
Total Power Consumption ◽  
Application Requirements ◽  
Online Methods

Energy efficiency in data center operation depends on many factors, including power distribution, thermal load and consequent cooling costs, and IT management in terms of how and where IT load is placed and moved under changing request loads. Current methods provided by vendors consolidate IT loads onto the smallest number of machines needed to meet application requirements. This paper’s goal is to gain further improvements in energy efficiency by also making such methods ‘spatially aware’, so that load is placed onto machines in ways that respect the efficiency of both cooling and power usage, across and within racks. To help implement spatially aware load placement, we propose a model-based reinforcement learning method to learn and then predict the thermal distribution of different placements for incoming workloads. The method is trained with actual data captured in a fully instrumented data center facility. Experimental results showing notable differences in total power consumption for representative application loads indicate the utility of a two-level spatially-aware workload management (SpAWM) technique in which (i) load is distributed across racks in ways that recognize differences in cooling efficiencies and (ii) within racks, load is distributed so as to take into account cooling effectiveness due to local air flow. The technique is being implemented using online methods that continuously monitor current power and resource usage within and across racks, sense BladeCenter-level inlet temperatures, understand and manage IT load according to an environment’s thermal map. Specifically, at data center level, monitoring informs SpAWM about power usage and thermal distribution across racks. At rack-level, SpAWM workload distribution is based on power caps provided by maximum inlet temperatures determined by CRAC speeds and supply air temperature. SpAWM can be realized as a set of management methods running in VMWare’s ESXServer virtualization infrastructure. Its use has the potential of attaining up to 32% improvements on the CRAC supply temperature requirement compared to non-spatially aware techniques, which can lower the inlet temperature 2∼3°C, that is to say we can increase the CRAC supply temperature 2∼3°C to save nearly 13% −18% cooling energy.

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