Impact of active tiles on data center flow and temperature distribution

Athavale, Jayati; Joshi, Yogendra; Yoda, Minami; Phelps, Wally

doi:10.1109/itherm.2016.7517680

Cited by 15 publications

(7 citation statements)

References 7 publications

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“…As mentioned previously, an ANN-based model for rapid temperature prediction in data center is used here. This model was developed using training data by conducting simulations using an experimentally validated CFD model for data center lab described in Section 2 (Athavale et al , 2018c, 2016). The model can be mathematically represented by the following equation: where T rack , inlet is the rack inlet temperature distribution, T a , ret and N CRAC are the return air temperature and blower speed cooling set-points of the CRAC unit, respectively and trueQ˙room is the IT heat load in the room.…”

Section: Frameworkmentioning

confidence: 99%

Genetic algorithm based cooling energy optimization of data centers

Athavale

Yoda

Joshi

2021

HFF

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Purpose This study aims to present development of genetic algorithm (GA)-based framework aimed at minimizing data center cooling energy consumption by optimizing the cooling set-points while ensuring that thermal management criteria are satisfied. Design/methodology/approach Three key components of the developed framework include an artificial neural network-based model for rapid temperature prediction (Athavale et al., 2018a, 2019), a thermodynamic model for cooling energy estimation and GA-based optimization process. The static optimization framework informs the IT load distribution and cooling set-points in the data center room to simultaneously minimize cooling power consumption while maximizing IT load. The dynamic framework aims to minimize cooling power consumption in the data center during operation by determining most energy-efficient set-points for the cooling infrastructure while preventing temperature overshoots. Findings Results from static optimization framework indicate that among the three levels (room, rack and row) of IT load distribution granularity, Rack-level distribution consumes the least cooling power. A test case of 7.5 h implementing dynamic optimization demonstrated a reduction in cooling energy consumption between 21%–50% depending on current operation of data center. Research limitations/implications The temperature prediction model used being data-driven, is specific to the lab configuration considered in this study and cannot be directly applied to other scenarios. However, the overall framework can be generalized. Practical implications The developed framework can be implemented in data centers to optimize operation of cooling infrastructure and reduce energy consumption. Originality/value This paper presents a holistic framework for improving energy efficiency of data centers which is of critical value given the high (and increasing) energy consumption by these facilities.

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Section: Frameworkmentioning

confidence: 99%

Genetic algorithm based cooling energy optimization of data centers

Athavale

Yoda

Joshi

2021

HFF

Self Cite

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“…A Green data center can be built using two primary methods that complement each other; first, the data center is designed using Green equipment; second, Green best practices are implemented as part of the daily operations in the new data center [6]. Previous studies have reported the advantages of using both methods including enhanced thermal control and cooling management, better utilization of data center resources, reduced power usage, and improved management of data center infrastructure [6], [27], [28].…”

Section: Green Data Centermentioning

confidence: 99%

Capacity Planning for Green Data Center Sustainability

Ramli

Jambari

2018

International Journal on Advanced Science, Engineering and Information Technology

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Business demands for powerful computing resources are increasing the workload in data centers, hence raising the complexity of managing the data center. What the impact of data centers on the environment is now a particular concern. Although there are now efforts to realize Green data centers through harnessing Green technologies, their sustainability remains an issue. Data center management includes planning its capacity. However, capacity planning for data center efficiency has only focused on measurements of power and cooling (part of infrastructure operation), which are insufficient to ensure the sustainability of the data center. In fact, a more thorough set of components must be considered. Therefore, this study proposes a capacity planning framework to derive practical solutions for Green data center management and thus ensure the sustainability of the Green data center. To this end, the components for Green data center sustainability and capacity planning were determined using Content Analysis and the findings verified by Green data center experts.

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“…Their models are capable of estimating energy consumption and heat transfer phenomenon. Some experiments [27][28][29] leveraged the active tiles to balance cooling airflow velocity between under-provisioning and overprovisioning. With the assistance of active tile fans, they tried to exactly provide the airflow volume required by servers.…”

Section: Cooling Subsystem Managementmentioning

confidence: 99%

“…5 indicate that although the air flow temperature at CRAC outlet is generally lower than other locations, the temperature is rather stable in server racks, and there is no strong correlation between locations and air flow temperature. In addition, recent advanced techniques like active tiles [29] and the Down-flow Plenum [46] further diminished the nonuniform temperature distribution. Therefore, our thermal model (8) is applicable in practice.…”

Section: Qos Constraintmentioning

confidence: 99%

Dynamic thermal and IT resource management strategies for data center energy minimization

Wan

Yang³

et al. 2017

J Cloud Comp

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Energy-hungry data centers attract researchers' attention to energy consumption minimization-a challenge confronted by both industry and academia. To assure the server reliability, its instantaneous temperature is generally controlled within a preset threshold. Nonetheless, field studies indicated that the occasional violation of extreme temperature constraints hardly affect the system reliability in practice. Therefore, strictly restraining the server temperature may contribute to meaningless energy consumption. As a response to this limitation, this paper presents a dynamic control algorithm without violating the average temperature constraint. We formulate a "soft" Server Temperature-Constrained Energy Minimization (STCEM) problem, where the object function consists of IT and Heat Ventilation & Air Conditioner (HVAC) energy. Based on the Lyapunov optimization, two algorithms, i.e., linear and quadratic control policies, are proposed to approximately solve the STCEM problem. The non-negative weight parameter V is introduced to trade off the energy consumption against server temperature constraint. Furthermore, extensive simulations have been carried out to evaluate the system performance for the proposed controlled algorithms. The experimental results demonstrate that the quadratic control policy outperforms the linear counterpart on the STCEM problem. Specifically, the energy consumption and server temperature constraint are well balanced when V ≈ 5000.

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Impact of active tiles on data center flow and temperature distribution

Cited by 15 publications

References 7 publications

Genetic algorithm based cooling energy optimization of data centers

Genetic algorithm based cooling energy optimization of data centers

Capacity Planning for Green Data Center Sustainability

Dynamic thermal and IT resource management strategies for data center energy minimization

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