A Machine Learning Solution for Data Center Thermal Characteristics Analysis

Grishina, Anastasiia; Chinnici, Marta; Kor, Ah-Lian; Rondeau, Éric; Georges, Jean‐Philippe

doi:10.3390/en13174378

Cited by 14 publications

(4 citation statements)

References 31 publications

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“…Other efforts include research by Haghshenas et al [3] that utilized a multi-agent machine learning (ML) strategy for energy-efficient virtual server consolidation and a study by Z. Yang et al [11] that implemented a light gradient boosting machine, a recurrent neural network, and random forests to optimize the energy efficiency of the total input energy per year of a DC by 0.24%. Another study [12,13] utilized machine learning thermal modeling to enhance DC energy efficiency, and many more efforts have been implemented to automate and optimize DC operations.…”

Section: Related Workmentioning

confidence: 99%

AI for Automating Data Center Operations: Model Explainability in the Data Centre Context Using Shapley Additive Explanations (SHAP)

Gebreyesus,

Dalton,

De Chiara

et al. 2024

Electronics

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The application of Artificial Intelligence (AI) and Machine Learning (ML) models is increasingly leveraged to automate and optimize Data Centre (DC) operations. However, the interpretability and transparency of these complex models pose critical challenges. Hence, this paper explores the Shapley Additive exPlanations (SHAP) values model explainability method for addressing and enhancing the critical interpretability and transparency challenges of predictive maintenance models. This method computes and assigns Shapley values for each feature, then quantifies and assesses their impact on the model’s output. By quantifying the contribution of each feature, SHAP values can assist DC operators in understanding the underlying reasoning behind the model’s output in order to make proactive decisions. As DC operations are dynamically changing, we additionally investigate how SHAP can capture the temporal behaviors of feature importance in the dynamic DC environment over time. We validate our approach with selected predictive models using an actual dataset from a High-Performance Computing (HPC) DC sourced from the Enea CRESCO6 cluster in Italy. The experimental analyses are formalized using summary, waterfall, force, and dependency explanations. We delve into temporal feature importance analysis to capture the features’ impact on model output over time. The results demonstrate that model explainability can improve model transparency and facilitate collaboration between DC operators and AI systems, which can enhance the operational efficiency and reliability of DCs by providing a quantitative assessment of each feature’s impact on the model’s output.

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Section: Related Workmentioning

confidence: 99%

AI for Automating Data Center Operations: Model Explainability in the Data Centre Context Using Shapley Additive Explanations (SHAP)

Gebreyesus,

Dalton,

De Chiara

et al. 2024

Electronics

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“…The thermodynamics impose limits on both the maximum allowable temperature of the microprocessors and the coefficient of performance of the heat pumps [ 27 , 28 ]. Setting higher temperature setpoints in the server room is proposed and used to improve the quality of the recovered heat [ 4 , 29 ]. In this case, accurate thermal models of the server room are developed to predict the temperature variations, detect the formation of hot spots which may lead to equipment malfunctioning, and evaluate alternatives in cooling system configurations [ 3 , 11 ].…”

Section: Related Workmentioning

confidence: 99%

“…Tradeoffs should be done among the accuracy of the simulation, execution time, and resource overheads. Mathematical models and machine-leaning-based approaches are used to address such tradeoffs [ 4 , 29 , 34 ] with varying levels of success. Mathematical models of the temperature evolution in a server room are presented in [ 35 , 36 ] addressing the thermal behavior concerning heat generation, circulation, and air-cooling system using Navier–Stokes equations expressing thermal laws or by using fast approximate solvers [ 37 , 38 ].…”

Section: Related Workmentioning

confidence: 99%

Heating Homes with Servers: Workload Scheduling for Heat Reuse in Distributed Data Centers

Antal

Cristea²,

Pădurean

et al. 2021

Sensors

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Data centers consume lots of energy to execute their computational workload and generate heat that is mostly wasted. In this paper, we address this problem by considering heat reuse in the case of a distributed data center that features IT equipment (i.e., servers) installed in residential homes to be used as a primary source of heat. We propose a workload scheduling solution for distributed data centers based on a constraint satisfaction model to optimally allocate workload on servers to reach and maintain the desired home temperature setpoint by reusing residual heat. We have defined two models to correlate the heat demand with the amount of workload to be executed by the servers: a mathematical model derived from thermodynamic laws calibrated with monitored data and a machine learning model able to predict the amount of workload to be executed by a server to reach a desired ambient temperature setpoint. The proposed solution was validated using the monitored data of an operational distributed data center. The server heat and power demand mathematical model achieve a correlation accuracy of 11.98% while in the case of machine learning models, the best correlation accuracy of 4.74% is obtained for a Gradient Boosting Regressor algorithm. Also, our solution manages to distribute the workload so that the temperature setpoint is met in a reasonable time, while the server power demand is accurately following the heat demand.

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“…Google has implemented a simple neural network ML approach for predicting Power Usage Effectiveness (PUE), which assisted in configuring controllable parameters and resulted in a 40% cooling efficiency [6]. Other research by A. Grishina et al, [7] was also conducted on thermal characterization and analysis using ML to enhance DC energy efficiency. Even though many more AI and ML-based research studies have been conducted to optimize DC energy efficiency and operations at different layers, relevant feature selection has been rarely discussed.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Data Center Optimizations: Feature Selection Using SHapley Additive exPlanation (SHAP)

Chinnici¹,

Gebreyesus²,

DALTON³

et al. 2022

Preprint

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The need for Artificial Intelligence (AI) and Machine Learning (ML) technologies is increasingly being leveraged for optimizing Data centers’ (DCs’) operations as the volume of operations management data increase tremendously. These strategies can assist operators in better understanding their DC operations and making informed decisions up front to preserve service reliability and availability. Aiming at creating models that optimize energy efficiency, identify inefficient resource utilization and scheduling policies, and predict outages. Apart from model hyperparameter tuning, feature selection is a crucial step to identify relevant features with the objective of providing insight into the data, improving performance, and reducing computational expenses. Although several feature selection methods have been discussed in various domains, none have been discussed in the context of the data center. This paper introduces SHapley Additive exPlanation (SHAP), a class of additive feature attribution values-based feature selection that is rarely discussed in literature. We compared the effectiveness of SHAP method with several widely used methods. We used a real DC dataset obtained from the ENEA CRESCO6 cluster with 2,0832 cores to evaluate the methods. To demonstrate the comparison of the methods, we picked the top 10 most important features from each method, the predictions were retrained, and their performance was evaluated using MAE, RMSE, and MPAE. The results show that the SHAP-assisted feature selection performed best and align with human intuition.

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A Machine Learning Solution for Data Center Thermal Characteristics Analysis

Cited by 14 publications

References 31 publications

AI for Automating Data Center Operations: Model Explainability in the Data Centre Context Using Shapley Additive Explanations (SHAP)

AI for Automating Data Center Operations: Model Explainability in the Data Centre Context Using Shapley Additive Explanations (SHAP)

Heating Homes with Servers: Workload Scheduling for Heat Reuse in Distributed Data Centers

Machine Learning for Data Center Optimizations: Feature Selection Using SHapley Additive exPlanation (SHAP)

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