Real time thermal management controller for data center

Shu, Zhang; Zhou, Tianyu; Ahuja, Nishi; Refai-Ahmed, Gamal; Zhu, Yixin; Chen, Guofeng; Wang, Zhihua; Song, Weiwei

doi:10.1109/itherm.2014.6892436

Cited by 6 publications

(3 citation statements)

References 2 publications

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“…Existing DC-related thermal management research highlights the primary challenges of cooling systems in high power density DCs [14]; recommends a list of thermal management strategies based on energy consumption awareness [2,15]; explores the effect of different cooling approaches on power usage effectiveness (PUE) using direct air with a spray system that evaporates water to cool and humidify incoming air [16]; investigates the thermal performance of air-cooled data centers with raised and non-raised floor configurations [17]; studies various thermofluid mechanisms using cooling performance metrics [18]; proposes thermal models for joint cooling and workload management [19], while other strains of research explore thermal-aware job scheduling, dynamic resource provisioning, and cooling [20]. In addition, server-related thermal information, such as inlet/outlet air temperature and air mover speed, is utilized to create thermal and power maps with the ultimate goal to monitor the real-time status of a DC [21].…”

Section: Background and Related Workmentioning

confidence: 99%

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A Machine Learning Solution for Data Center Thermal Characteristics Analysis

et al. 2020

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The energy efficiency of Data Center (DC) operations heavily relies on a DC ambient temperature as well as its IT and cooling systems performance. A reliable and efficient cooling system is necessary to produce a persistent flow of cold air to cool servers that are subjected to constantly increasing computational load due to the advent of smart cloud-based applications. Consequently, the increased demand for computing power will inadvertently increase server waste heat creation in data centers. To improve a DC thermal profile which could undeniably influence energy efficiency and reliability of IT equipment, it is imperative to explore the thermal characteristics analysis of an IT room. This work encompasses the employment of an unsupervised machine learning technique for uncovering weaknesses of a DC cooling system based on real DC monitoring thermal data. The findings of the analysis result in the identification of areas for thermal management and cooling improvement that further feeds into DC recommendations. With the aim to identify overheated zones in a DC IT room and corresponding servers, we applied analyzed thermal characteristics of the IT room. Experimental dataset includes measurements of ambient air temperature in the hot aisle of the IT room in ENEA Portici research center hosting the CRESCO6 computing cluster. We use machine learning clustering techniques to identify overheated locations and categorize computing nodes based on surrounding air temperature ranges abstracted from the data. This work employs the principles and approaches replicable for the analysis of thermal characteristics of any DC, thereby fostering transferability. This paper demonstrates how best practices and guidelines could be applied for thermal analysis and profiling of a commercial DC based on real thermal monitoring data.

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Section: Background and Related Workmentioning

confidence: 99%

“…A majority of previously listed research work focuses on simulations or numerical modeling [2,[16][17][18][19][20] as well as on empirical studies involving R&D or small-scale data centers [16,21]. Thus, there is a need for more empirical research involving real thermal-related data for large scale data centers.…”

Section: Background and Related Workmentioning

confidence: 99%

A Machine Learning Solution for Data Center Thermal Characteristics Analysis

et al. 2020

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“…It encompasses compliance of IT facility environment to temperature requirements and standards that will inevitably result in reliability, availability, and overall improved server performance. Existing data center-related thermal management research: highlight the primary challenges of cooling high power density DCs [8]; recommend a list of thermal management strategies based [9]; experiment the effect of a cooling approach on PUE, using direct air with a spray system that evaporates water to cool as well as humidify incoming air [10]; investigate thermal performances of air-cooled data centers with raised and non-raised floor configurations [11] and quantification of thermos-fluid processes through performance metrics [12]; propose a thermal model for joint cooling and workload management [13] while [14] explore thermal-aware job scheduling, dynamic resource provisioning and cooling; utilise real thermal information about servers, inlet/outlet air temperature, air mover speed to create thermal and power maps to monitor the real-time status of a data center [15]. Majority of the previously listed research focuses on simulations or numerical modelling [9][10][11][12][13][14], empirical research involving R&D or small scale data center [10,15] and thus there is a need for more empirical research involving real relevant thermal-related data for big scale data centers.…”

Section: Background and Related Workmentioning

confidence: 99%

A Machine Learning Solution for Data Center Thermal Characteristics Analysis

Chinnici¹,

Grishina²,

Kor³

et al. 2020

Preprint

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Energy efficiency of Data Center (DC) operations heavily relies on IT and cooling systems performance. A reliable and efficient cooling system is necessary to produce a persistent flow of cold air to cool servers that are subjected to constantly increasing computational load due to the advent of IoT- enabled smart systems. Consequently, increased demand for computing power will bring about increased waste heat dissipation in data centers. In order to bring about a DC energy efficiency, it is imperative to explore the thermal characteristics analysis of an IT room (due to waste heat). This work encompasses the employment of an unsupervised machine learning modelling technique for uncovering weaknesses of the DC cooling system based on real DC monitoring thermal data. The findings of the analysis result in the identification of areas for energy efficiency improvement that will feed into DC recommendations. The methodology employed for this research includes statistical analysis of IT room thermal characteristics, and the identification of individual servers that frequently occur in the hotspot zones. A critical analysis has been conducted on available big dataset of ambient air temperature in the hot aisle of ENEA Portici CRESCO6 computing cluster. Clustering techniques have been used for hotspots localization as well as categorization of nodes based on surrounding air temperature ranges. The principles and approaches covered in this work are replicable for energy efficiency evaluation of any DC and thus, foster transferability. This work showcases applicability of best practices and guidelines in the context of a real commercial DC that transcends the set of existing metrics for DC energy efficiency assessment.

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Thermal management of air and liquid cooled data centres: A review

Chethana

Gowda²

2021

Materials Today: Proceedings

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Real time thermal management controller for data center

Cited by 6 publications

References 2 publications

A Machine Learning Solution for Data Center Thermal Characteristics Analysis

A Machine Learning Solution for Data Center Thermal Characteristics Analysis

A Machine Learning Solution for Data Center Thermal Characteristics Analysis

Thermal management of air and liquid cooled data centres: A review

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