Green IT scheduling for data center powered with renewable energy

Grange, Léo; Costa, Georges Da; Stolf, Patricia

doi:10.1016/j.future.2018.03.049

Cited by 61 publications

(28 citation statements)

References 42 publications

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“…This has resulted in a constant strain of environmental and economic resources because of the energy consumed by these systems. This is reflective in a report presented on energy consumption that in the year 2012, computer data centers consumed 270 TWh, making up 1.4% of the total global energy consumption (Leo Grange et al, 2018). It has also been discovered that the next level of energy consumption will be of a tremendous growth compared to the former.…”

Section: Introductionmentioning

confidence: 94%

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Preliminary experimental of GPU immersion-cooling

et al. 2019

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A typical information technology system takes around 40% of the total energy used in cooling the system. There are three major classifications of cooling system and they are: water cooling, close loop liquid cooling, and immersion cooling systems. Immersion cooling has been observed to be the latest trend in cooling systems for IT devices. It is a cooling procedure that is carried out through the immersion of all computer components in a dielectric coolant. This research examined the cooling process of GPU using this immersion method. Mineral oil, because of its high dielectric strength, is used as a medium fluid. The temperature difference between the use of fan and immersion cooling was then measured using a benchmark software. The result showed that the immersion cooling produced a lower GPU temperature compared to the conventional fan. The working temperature of the GPU with the use of immersion method was 70°C while it was 80°C with the conventional fan method.

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Section: Introductionmentioning

confidence: 94%

“…A typical information technology system would have a mechanism to cool itself and this system takes around 40% of the total energy used in cooling the system [2]. There are three major classifications of cooling system in computer components and they are: water cooling, close loop liquid cooling, and immersion cooling systems.…”

Section: Introductionmentioning

confidence: 99%

Preliminary experimental of GPU immersion-cooling

et al. 2019

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“…For short-time forecasting, a recursive autoencoder is used onto fine-grained models while for long-term prediction massive fine-grained historical data are encoded into the coarse-grained model. According to [21] the intermittent nature of renewable energy sources is seen as a major drawback of using it on DCs site. The authors propose a scheduler that uses IT and electrical models of the DC energy consumption together with an energy availability prediction engine for the next 48 h. In [22] a multi-layered ANN is defined and used to forecast the DC energy consumption on monthly intervals based on the historical energy consumption data.…”

Section: Related Workmentioning

confidence: 99%

Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs

et al. 2020

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In this paper, we address the problem of the efficient and sustainable operation of data centers (DCs) from the perspective of their optimal integration with the local energy grid through active participation in demand response (DR) programs. For DCs’ successful participation in such programs and for minimizing the risks for their core business processes, their energy demand and potential flexibility must be accurately forecasted in advance. Therefore, in this paper, we propose an energy prediction model that uses a genetic heuristic to determine the optimal ensemble of a set of neural network prediction models to minimize the prediction error and the uncertainty concerning DR participation. The model considers short term time horizons (i.e., day-ahead and 4-h-ahead refinements) and different aspects such as the energy demand and potential energy flexibility (the latter being defined in relation with the baseline energy consumption). The obtained results, considering the hardware characteristics as well as the historical energy consumption data of a medium scale DC, show that the genetic-based heuristic improves the energy demand prediction accuracy while the intra-day prediction refinements further reduce the day-ahead prediction error. In relation to flexibility, the prediction of both above and below baseline energy flexibility curves provides good results for the mean absolute percentage error (MAPE), which is just above 6%, allowing for safe DC participation in DR programs.

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“…Additionally, the stakes are very high for data centre managers. Several researchers and policy makers have put a spotlight on the carbon footprint of the IT organizations and their data centres [7]. Operators had to adapt and reveal to the general public their energy consumption, their method of cooling, and more generally, the very conception of their layout and architecture [8].…”

Section: Introductionmentioning

confidence: 99%

Improving Airflow and Thermal Distribution in a Real Data Centre Room Through Computational Fluid Dynamics Modeling

Macedo

Gaspar²,

Silva³

et al. 2019

2019 8th International Conference on Industrial Technology and Management (ICITM)

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A Data Centre is a physical space that groups together computer equipment such as servers, storage arrays, among other equipment. It can be used for storage, processing and data protection. The majority of data centres operate 24 hours a day, and must provide the user with guarantees in terms of security and performance. Energy consumption is therefore permanent and the level of assistance and maintenance high. Data Centres have an ecological impact that is almost invisible to many users. In this paper a computational fluid dynamics (CFD) model of the airflow and thermal distribution of a real data centre room with 208 racks is proposed. Two case studies are presented and simulated with a high thermal load subjected to the minimum and maximum air flow velocity, respectively. The objective is to assess if the computer room air conditioning unit (CRAC) can cool the racks of the data centre in order to efficiently refrigerate all the hot spots.

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Green IT scheduling for data center powered with renewable energy

Abstract: Green IT scheduling for data center powered with renewable energy. (2018) Future Generation Computer Systems-FGCS, 86. 99-120.

Cited by 61 publications

References 42 publications

Preliminary experimental of GPU immersion-cooling

Preliminary experimental of GPU immersion-cooling

Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs

Improving Airflow and Thermal Distribution in a Real Data Centre Room Through Computational Fluid Dynamics Modeling

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