A Scalable Digital Twin Framework for Energy Optimization in Data Centers

TL;DR

This paper introduces a scalable Digital Twin framework utilizing IoT, cloud computing, and machine learning to optimize energy use in data centers, demonstrated through a small-scale environment with promising results.

Contribution

It presents a novel scalable Digital Twin framework that combines IoT, cloud, and machine learning for real-time energy management in data centers.

Findings

Energy consumption reduced in experimental setup

Improvements in Power Usage Effectiveness (PUE)

Effective energy demand forecasting with LSTM models

Abstract

This study proposes a scalable Digital Twin framework for energy optimization in data centers.The framework integrates IoT-based data acquisition, cloud computing, and machine learning techniques to enable real-time monitoring, forecasting, and intelligent energy management. A controlled small-scale data center environment was developed to monitor variables such as power consumption, temperature, and computational workload. Long Short-Term Memory (LSTM) models were employed to predict energy demand and support operational decision-making. Experimental results demonstrated improvements in energy efficiency, including reductions in power consumption and enhancements in Power Usage Effectiveness (PUE). Despite being evaluated in a constrained environment, the proposed framework demonstrates strong potential as a scalable and cost-effective solution for sustainable data center management.