Data Mining for Big Dataset-Related Thermal Analysis of High Performance Computing (HPC) Data Center

Chiara, Davide De; Chinnici, Marta; Kor, Ah-Lian

doi:10.1007/978-3-030-50436-6_27

Cited by 4 publications

(7 citation statements)

References 12 publications

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“…Revise cluster load scheduling so that these frequently overheated servers are not overloaded in the future (note: this is to enable an even thermal distribution within the IT room. See [11] for details). In other words, it is recommended to formulate a resource allocation policy for the purpose of a more even thermal distribution of ambient air temperature; 3.…”

Section: Discussionmentioning

confidence: 99%

“…This paper is an extension of the previous authors' work [10,11,[24][25][26][27][28], which focus on real DC thermal monitoring data. In detail, this current research focuses on the analysis of DC IT room thermal characteristics to uncover ways to render a more effective cooling system as well as explore possibilities to employ machine learning techniques to address this issue.…”

Section: Background and Related Workmentioning

confidence: 98%

“…Thus, it is necessary to address the issue of server waste heat dissipation with the ultimate goal to attain an even thermal distribution within a premise. This is possible by taking appropriate measures to prevent the emergence of heat islands that result in individual server overheating [11].…”

Section: Introductionmentioning

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: Discussionmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 98%

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A Machine Learning Solution for Data Center Thermal Characteristics Analysis

et al. 2020

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“…CRAC was adopted in Refs. [5][6][7][8][9][10] without any mention of measuring the system's efficiency. By contrast, in Refs.…”

Section: Cooling Modelmentioning

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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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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“…health, manufacturing, entertainment, etc…). In other words, a data center has emerged as a mission-critical infrastructure [30] to the survival of businesses and other smart technologies supported sectors. Therefore, it warrants a critical necessity for backup system management and uninterruptible power supply (UPS) systems so that compute system stability can be maintained even in emergency situations.…”

Section: Introductionmentioning

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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Data Mining for Big Dataset-Related Thermal Analysis of High Performance Computing (HPC) Data Center

Cited by 4 publications

References 12 publications

A Machine Learning Solution for Data Center Thermal Characteristics Analysis

A Machine Learning Solution for Data Center Thermal Characteristics Analysis

Research advances on AI-powered thermal management for data centers

A Machine Learning Solution for Data Center Thermal Characteristics Analysis

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