Simulator for modeling, analysis, and visualizations of thermal status in data centers

Ullah, Rahmat; Ahmad, Naveed; Malik, Saif Ur Rehman; Akbar, Saeed; Anjum, Adeel

doi:10.1016/j.suscom.2017.12.005

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…Well-known simulators have been used in many studies to evaluate their work, such as CloudSim, CloudAnalyst, MDCIM, GDCim, and the widely used model CFD. Most of these simulators were developed in Java, C++, and Python [33] . Some benchmark systems were proposed to evaluate thermal efficiency, such as the ThermoBench framework shown in Fig.…”

Section: Simulationmentioning

confidence: 99%

“…Simulators were proposed, such as the simulator introduced by Ullah et al [33] , which takes DC computational devices as input and produces models that allow the analysis, visualization, and prediction of DC thermal conditions and detect the origin reason of hot spots. The simulator allows for applying different job allocation strategies with the ability to analyze each strategy's state and find the best strategy for job allocation.…”

Section: Simulationmentioning

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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Section: Simulationmentioning

confidence: 99%

Section: Simulationmentioning

confidence: 99%

Research advances on AI-powered thermal management for data centers

Liu

Aljbri

Song

et al. 2022

Tsinghua Sci. Technol.

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“…By using real-time visualization of an entire data center, the thermal transients of individual servers can be assessed, which helps to determine sources of potential hotspots before they occur i.e. servers exceeding their specified temperature threshold [15].…”

Section: Data Center Computational Fluid Dynamicsmentioning

confidence: 99%

Validated Thermal Air Management Simulations of Data Centers Using Remote Graphics Processing Units

Sjölund

Vesterlund

Delbosc³

et al. 2018

IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society

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Simulation tools for thermal management of data centers help to improve layout of new builds or analyse thermal problems in existing data centers. The development of LBM on remote GPUs as an approach for such simulations is discussed making use of VirtualGL and prioritised multi-threaded implementations of an existing LBM code. The simulation is configured to model an existing and highly monitored test data center. Steady-state root mean square averages of measured and simulated temperatures are compared showing good agreement. The full capability of this simulation approach is demonstrated when comparing rack temperatures against a time varying workload, which employs time-dependent boundary conditions.

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