Thermal-aware Flow Field Optimization for Energy Saving of Data Centers

Wang, Qi; Song, Mengxuan; Fang, Qiu; Wang, Jun

doi:10.23919/acc.2018.8430975

Cited by 3 publications

(5 citation statements)

References 12 publications

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“…Adapting the power of the cooling system could be another direction to go. Very advanced techniques have been designed in related works 26,53,54 to better control the power of the cooling system (e.g., by adapting the speed of the fans to the temperature). The predictions made by our solution could be used as an additional input to take the right decisions regarding the control of the cooling system.…”

Section: Discussionmentioning

confidence: 99%

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CPU overheating prediction in HPC systems

Platini

Ropars

Pelletier

et al. 2021

Concurrency and Computation

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Summary With the increase in size of supercomputers, also increases the number of abnormal events. CPU overheating is one such event that decreases the system efficiency: when a CPU overheats, it reduces its frequency. This paper presents a machine learning solution to predict such events. The proposed algorithm is based on dynamic time warping for feature extraction and on a machine learning algorithm for classification. It predicts overheating events solely by analyzing the trends of the temperature of the CPUs and can deal with very low temperature sampling rates while having a negligible computational cost in practice. Our evaluation, using data coming from a production supercomputer, shows that the proposed solution can make predictions a few minutes in advance with a good accuracy. Furthermore, considering two simple preventive actions to avoid CPU overheating events, we present an analytical study that shows that our predictive solution is good enough to allow a significant reduction of the cost of overheating events.

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Section: Discussionmentioning

confidence: 99%

“…Another direction to go to improve our prediction capabilities would be to take into account heat dissipation phenomenons to build models for temperature prediction in different points of the computer room 54 . Inputs coming from the cooling system, such as the temperature of the water, could also be taken into account.…”

Section: Discussionmentioning

confidence: 99%

CPU overheating prediction in HPC systems

Platini

Ropars

Pelletier

et al. 2021

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…Chaudhry et al [18] used the CRAC and explained the effect of a rise in the inlet temperature of each server. Wang et al [19] proposed that the key challenge was to reduce the power utilized in CRACs by combining the cooling system and computing equipment. In this study, fans were installed on the CRACs and racks, and the computer room's layout can determine the flow pattern's reorganization.…”

Section: Cooling Modelmentioning

confidence: 99%

“…TSTD achieves power consumption saving of approximately 15.2%-42.7% compared to the FCFS algorithm, except if the core number was 4000 with 0% or 100% of utilization. Wang et al [19] applied the particle swarm optimization (PSO) algorithm to solve the flow rate optimization problem due to its complexity and nonlinearity. As aforementioned, Oxley et al [16] used a GA-based approach in addition to their previous study [40] and a greedy heuristic.…”

Section: Intelligent Optimizationmentioning

confidence: 99%

“…The proposed task scheduling algorithm saves as much as 11% power consumption. The reinforcement learning (RL) model was applied by Wang et al [19] for the outer layer to define the influence of thermal changes on their proposed model temperature rising matrix (TRM) for different flow rate distributions. The proposed learning model reflects the influence of fans' airflow rates on TRM parameters to overcome the relationship complexity between the predicted steady temperature and flow fields.…”

Section: Learning and Miningmentioning

confidence: 99%

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Research advances on AI-powered thermal management for data centers

Liu

Aljbri

Song

et al. 2022

Tsinghua Sci. Technol.

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Given the complex nature of data centers' thermal management, which costs too many resources, processing time, and energy consumption, thermal awareness and thermal management powered by artificial intelligence (AI ) are the targeted study. In addition to a few research on AI techniques and models, other strategies have also been introduced in recent years. Data center models, including cooling, thermal, power, and workload models, and their relationship are factors that need to be understood in the optimal thermal management system.Simulation approaches have been proposed to help validate new models or methods used for scheduling and consolidating processes and virtual machines (VMs), hotspot identification, thermal state estimation, and power usage change. AI-powered thermal optimization leads to improved process scheduling and consolidation of VMs and eliminates the hotspot from happening. At present, research on AI-powered thermal control is still in its infancy. This paper concludes with four issues in thermal management, which will be the scope of further research.

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Simulators for Conversing Power to Thermal on Green Data Centers: A Review

Li,

Song,

Liu

et al. 2024

Energies

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This paper aims to help data center administrators choose thermal simulation tools, which manage thermal conduction from power for energy savings. When evaluating and suggesting data center thermal simulators for users, questions such as “What are the simulator’s differences? Are they easy to use? Which is the best choice?” are frequently asked. To answer these questions, this paper reviews the thermal simulation works for data centers in the last ten years. After that, it proposes the versatility and dexterity metrics for these simulators and discovers that it is difficult to choose them despite their similar design purpose and functions. Empowered by the survey, we claim that the widespread practice simulators still need more enhancement in data center scenarios. We back up our claim by comparing typical simulators and propose improvements to thermal simulators for future studies.

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Thermal-aware Flow Field Optimization for Energy Saving of Data Centers

Cited by 3 publications

References 12 publications

CPU overheating prediction in HPC systems

CPU overheating prediction in HPC systems

Research advances on AI-powered thermal management for data centers

Simulators for Conversing Power to Thermal on Green Data Centers: A Review

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