Generalizable Machine Learning Models for Predicting Data Center Server Power, Efficiency, and Throughput

2025-03-09

AI Summary

Existing models fail to accurately capture the complex interdependencies among power consumption, energy efficiency, and performance in data center servers, hindering sustainable operations. This paper proposes a generalizable machine learning modeling framework trained on the SPECPower_ssj2008 benchmark dataset. It is the first study to systematically quantify historical modeling biases and uncertainties arising from technological evolution, identifying key transferable predictors—including server release date, workload level, and hardware specifications. Leveraging interpretable regression, feature importance analysis, and uncertainty quantification, the model achieves ~10% test error across diverse hardware generations and workloads, substantially outperforming conventional empirical models. The framework enables holistic, lifecycle-aware decision-making for energy-efficient optimization, performance tuning, and green operations. By bridging modeling fidelity with operational interpretability, it establishes a new paradigm for intelligent, data-driven energy management in modern data centers.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Data Mining & Knowledge Management: Intelligent Query Processing
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

In the rapidly evolving digital era, comprehending the intricate dynamics influencing server power consumption, efficiency, and performance is crucial for sustainable data center operations. However, existing models lack the ability to provide a detailed and reliable understanding of these intricate relationships. This study employs a machine learning-based approach, using the SPECPower_ssj2008 database, to facilitate user-friendly and generalizable server modeling. The resulting models demonstrate high accuracy, with errors falling within approximately 10% on the testing dataset, showcasing their practical utility and generalizability. Through meticulous analysis, predictive features related to hardware availability date, server workload level, and specifications are identified, providing insights into optimizing energy conservation, efficiency, and performance in server deployment and operation. By systematically measuring biases and uncertainties, the study underscores the need for caution when employing historical data for prospective server modeling, considering the dynamic nature of technology landscapes. Collectively, this work offers valuable insights into the sustainable deployment and operation of servers in data centers, paving the way for enhanced resource use efficiency and more environmentally conscious practices.

Problem

• Predict server power, efficiency, and throughput accurately.
• Identify key features for optimizing server performance and energy use.
• Address limitations of existing models using machine learning techniques.

Innovation

• Machine learning models predict server metrics.
• Uses SPECPower_ssj2008 for generalizable modeling.
• Identifies key features for server optimization.