Research progress and development trend of cross-layer energy efficiency optimization in data centers

Wang, Jiye; Zhou, Biyu; Liu, Wantao; Hu, Songlin

doi:10.1360/n112018-00293

Cited by 7 publications

(3 citation statements)

References 35 publications

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“…Several noteworthy investigations have yielded significant insights among these. The study undertaken by Wang et al (2020) delved into the comprehensive examination of artificial intelligence (AI) applications in the realms of conflict prediction and prevention. The study underscores the potential of machine learning algorithms in the analysis of historical data and the simulation of war scenarios.…”

Section: Literature Review Of Existing Methods and Models For Data-dr...mentioning

confidence: 99%

Building a sustainable peace and development model through data-driven Chinese peacekeeping actions based on UN Global Pulse

Yan,

Zhang

2024

J. Infras. Policy. Dev.

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This paper aims to explore how to build a sustainable peace and development model for China’s peacekeeping efforts through the application of data-driven methods from UN Global Pulse. UN Global Pulse is a United Nations agency dedicated to using big data and artificial intelligence technologies to address global challenges. In this paper, we will introduce the working principles of UN Global Pulse and its application in the fields of peacekeeping and development. Then, we will discuss the current situation of China’s participation in peacekeeping operations and how data-driven methods can help China play a greater role in peacekeeping tasks. Finally, we will propose a sustainable peace and development model that combines data-driven methods with the advantages of China’s peacekeeping efforts to achieve long-term peace and development goals.

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Section: Literature Review Of Existing Methods and Models For Data-dr...mentioning

confidence: 99%

Building a sustainable peace and development model through data-driven Chinese peacekeeping actions based on UN Global Pulse

Yan,

Zhang

2024

J. Infras. Policy. Dev.

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“…For data center refrigeration, heating, ventilation, air conditioning (HVAC), and computer room air conditioning (CRAC) are commonly applied, and many experiments and models have been developed for the accurate estimation of these processes. Some researchers have found that the overall consideration of the IT system layer and the use of a supporting facility layer for cross-layer optimization can maximize the energy-saving potential of the data center [ 22 , 23 ]. This research focused on the cross-layer energy consumption optimization of the data center, taking the modules as a whole and considering the relationships between modules and carrying out unified optimal scheduling and management.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Overall Energy Consumption of Data Centers in Different Locations

Zhang

Liu

2022

Sensors

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The use of big data leads to higher demands for hyperscale data centers (HDCs) in terms of the scale and quantity required for data storage and processing. Before the construction of an HDC, it is necessary to comprehensively analyze the economic budget according to the energy requirements and potential energy cost. We propose a global energy consumption prediction framework based on the power usage effectiveness (PUE) calculation that considers all heat sources and power consumption. The framework integrates physical models and a statistical framework that combines IT equipment energy consumption and data center energy consuming predictions. Furthermore, the framework provides a method to calculate the carbon emissions and electricity cost of the data center. Using hourly meteorological data as climate parameters, combined with a limited range of energy parameters, the annual PUE values of 60 regions were estimated, and a further analysis of the Carbon Usage Effectiveness (CUE) and electricity costs in China was conducted as an example. Based on experimental validation and an evaluation of real-time data, our framework can predict the overall energy consumption of HDCs effectively, filling a gap in HDC research in the Asia-Pacific region and providing a basis for HDC feasibility analysis.

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“…A considerable amount of literature has been published on reducing PUE, focusing on optimizing the DC design and location (Yue et al., 2018), job scheduling of IT facility (Beloglazov & Buyya, 2012; Jing‐Yu et al., 2019), decentralized power allocation schemes (Bistritz et al., 2019; Miller et al., 2015; Zhou & Bambos, 2015), air management systems (Fouladi et al., 2017), cooling systems (Hossain et al., 2017), etc. In recent years, the cross‐layer energy efficiency optimization methods have been studied, which include the cooling optimization via IT scheduling, power distribution optimization via IT scheduling, joint optimization of IT and cooling, and joint optimization of IT and power distribution (Wang et al., 2020). The cross‐layer methods aim at improving the power supply system efficiency with IT load dispatching and the combined efficiency improvement of the IT and cooling systems.…”

Section: Energy Efficiency Of Data Centersmentioning

confidence: 99%

Increasing the energy efficiency of a data center based on machine learning

Yang

Lin

et al. 2021

J of Industrial Ecology

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Energy efficiency of data centers (DCs) is of great concern due to their large amount of energy consumption and the foreseeable growth in the demand of digital services in the future. The past decade witnessed improvements of the energy efficiency of DCs from an extensive margin—a shift from small to large, more efficient DCs. Improvements from the intensive margin, that is, from more efficient operation, would be critical in limiting the energy consumption and environmental impact of DCs in the upcoming period. Machine learning algorithms have advantages in optimizing the operation of DCs to improve energy efficiency as they have shown the potential of discovering control strategies not found by traditional method, and producing working condition‐dependent control strategies. This study proposes ready‐to‐use machine learning methods with practical details to decrease the most commonly used energy efficiency metric—power usage effectiveness in DCs. We achieved an accurate prediction by properly selecting the features used in the proposed prediction models established by neural network, light gradient boosting machine, recurrent neural network, and random forests. The proposed approaches are implemented in one of the largest hyperscale DCs in China—Tencent Tianjin DC, to optimize the set points of controllable variables in the cooling system and to detect and adjust the unreasonable working conditions in the modular data centers. The lower bound of PUE reduction was 0.005 with the proposed approaches, leading to about 1500 MWh (0.24% of the total designed electricity consumption of this DC) of energy saved per year in this hyperscale DC. The proposed methods have the potential to be transferred to DCs of similar scale, and the framework of our work could serve as a guide for machine learning‐based optimization of environmental indicators in other complex product/service systems.

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Research progress and development trend of cross-layer energy efficiency optimization in data centers

Cited by 7 publications

References 35 publications

Building a sustainable peace and development model through data-driven Chinese peacekeeping actions based on UN Global Pulse

Building a sustainable peace and development model through data-driven Chinese peacekeeping actions based on UN Global Pulse

Prediction of Overall Energy Consumption of Data Centers in Different Locations

Increasing the energy efficiency of a data center based on machine learning

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