Thermal Piloting: A Novel Approach for Sensor Localization in Data Center Monitoring

Jafarizadeh, Mehdi; Tsai, Pei-Ying; Zheng, Rong

doi:10.1109/dcoss.2019.00026

Cited by 1 publication

(2 citation statements)

References 21 publications

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“…Several studies have formatted their optimization scheduling problem as NP-hard [6,9,15,28,[40][41][42] . Therefore, finding a near-optimal solution was the goal of these research works due to the complexity of the problem and the difficulty of finding the optimal solution.…”

Section: Optimizationmentioning

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: Optimizationmentioning

confidence: 99%

“…The supervised machine learning approaches were utilized by Jafarizadeh et al [42] for determining the location of any sensor among a set of probable locations. Classical supervised learning techniques, such as K-nearest neighbor or support vector machine, can be utilized.…”

Section: Learning and Miningmentioning

confidence: 99%