###### Abstract  
The need for artificial intelligence (AI) and machine learning (ML) models to optimize data center (DC) operations increases as the volume of operations management data upsurges tremendously. These strategies can assist operators in better understanding their DC operations and help them make informed decisions upfront to maintain service reliability and availability. The strategies include developing models that optimize energy efficiency, identifying inefficient resource utilization and scheduling policies, and predicting outages. In addition to model hyperparameter tuning, feature subset selection (FSS) is critical for identifying relevant features for effectively modeling DC operations to provide insight into the data, optimize model performance, and reduce computational expenses. Hence, this paper introduces the Shapley Additive exPlanation (SHAP) values method, a class of additive feature attribution values for identifying relevant features that is rarely discussed in the literature. We compared its effectiveness with several commonly used, importance-based feature selection methods. The methods were tested on real DC operations data streams obtained from the ENEA CRESCO6 cluster with 20,832 cores. To demonstrate the effectiveness of SHAP compared to other methods, we selected the top ten most important features from each method, retrained the predictive models, and evaluated their performance using the MAE, RMSE, and MPAE evaluation criteria. The results presented in this paper demonstrate that the predictive models trained using features selected with the SHAP-assisted method performed well, with a lower error and a reasonable execution time compared to other methods. data center; artificial intelligence; machine learning; feature selection; SHAP; game theory +
Footnote †: journal: _Future Internet_  
massive amounts of streaming data over time, allowing DC operators to make data-driven decisions. However, the massive amount of streaming data must be transformed into actionable information to optimize DC operations. Thus far, commonly used methods for modeling DC operations and analyzing data streams are based on heuristic, statistical, and mathematical models. These models are reactive and incapable of processing massive amounts of data streams with complex and non-linear interactions [5] in such a complex data center environment. Recently, artificial intelligence and machine learning technologies being increasingly leveraged in the data center industries to model and process massive amounts of streaming data into actionable information. Google 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 enhancement [6]. Research by [PERSON] et al. [7] was also conducted on thermal characterization and analysis using ML to enhance DC energy efficiency. Although 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 rarely been discussed. However, relevant feature selection is the backbone for effectively modeling DC operations with the objectives of improving model performance, reducing computational expense, and providing insight into the underlying patterns. To the best of our knowledge, although several feature selection methods have been discussed in various domains, they have rarely been discussed in the context of the DC industries. Therefore, identifying relevant features in the context of DCs is essential for mining the underlying patterns and effectively modeling DC operations. Hence, this paper establishes Shapley Additive exPlanation (SHAP) value-assisted Feature Selection (FS) method, which is rarely discussed in the literature. SHAP was initially proposed by [PERSON] and [PERSON] [8] for explainable AI (XAI) in the field of AI and machine learning techniques.