DC Energy Data Measurement and Analysis for Productivity and Waste Energy Assessment

Grishina, Anastasiia; Chinnici, Marta; Chiara, Davide De; Guarnieri, Guido; Kor, Ah-Lian; Rondeau, Éric; Georges, Jean‐Philippe

doi:10.1109/cse.2018.00008

Cited by 4 publications

(3 citation statements)

References 31 publications

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“…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: 97%

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: 97%

A Machine Learning Solution for Data Center Thermal Characteristics Analysis

et al. 2020

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“…This work focuses on the identification of individual servers in an IT room of a DC cluster that frequently occurs in the hotspot zones applying a clustering algorithm to an available dataset with thermal characteristics of ENEA Portici CRESCO6 computing cluster. This paper represents the completion of the previous authors' work [7,17,18,19,20,21,31] in terms of exploring the intricacies of deploying the theoretical framework applied in a real DC. Appropriate data analytics techniques have been based on real server-level sensors data to identify potential risks caused by the possible presence of negative covert factors related to the cooling strategy.…”

Section: Background and Related Workmentioning

confidence: 98%

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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“…Even though there are some carbon and hydro-based metrics such as Carbon Usage Effectiveness (CUE) and Water Usage Effectiveness (WUE), the deployment of these metrics in a real context is yet to be made. Regarding the productivity metrics (e.g., useful work), the works [3,4,5,7] have shown advance beyond state-of-the-art. On the other hand, the authors in [7] propose a methodology that addresses the problem of measurement, calculation, and evaluation of energy productivity assessment in a DC, which encompasses both the proportion of energy employed for computing and energy wasted during computational processing work.…”

Section: Related Workmentioning

confidence: 99%

Energy-Oriented Analysis of HPC Cluster Queues: Emerging Metrics for Sustainable Data Center

Grishina

Chinnici

Chiara

et al. 2019

Lecture Notes in Electrical Engineering

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This work analyzes a very subtle kind of energy metrics for Data Centers (DCs), namely productivity metrics which affect the global energy efficiency assessment in DC since they focus on the energy used for processing computing operations. By exploiting the available set of energy consumption data of operating systems in ENEA-DC, HPC-Cluster, the authors evaluated the energy consumed by different queues with several running applications. The queues energy waste has been calculated to provide an assessment for the ineffective use of computation-related energy load within the Cluster. This work shows an increment innovation beyond state-of-the-art for productivity metrics (e.g. useful work), and it will also help provide an invaluable insight into useful energy use and the use of enhanced sustainability metrics with the goal of driving a more sustainable DC. Additionally, sustainability concept in DC operations is driven by estimation of its indirect carbon emissions, which is shown in this work.

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DC Energy Data Measurement and Analysis for Productivity and Waste Energy Assessment

Cited by 4 publications

References 31 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

Energy-Oriented Analysis of HPC Cluster Queues: Emerging Metrics for Sustainable Data Center

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