OpenDT: Exploring Datacenter Performance and Sustainability with a Self-Calibrating Digital Twin

2026-04-13

AI Summary

This work addresses the absence of open-source, self-calibrating digital twin systems in existing data centers, which hinders real-time autonomous operations and dynamic optimization. To bridge this gap, we propose OpenDT—the first open, self-calibrating digital twin framework tailored for data centers—that integrates continuous telemetry streams, discrete-event simulation driven by real-world ICT workloads, and SLO-aware human-in-the-loop feedback to enable online modeling and optimization of both performance and energy efficiency. Designed in accordance with FAIR and FOSS principles, OpenDT supports online recalibration to enhance model fidelity. Experimental evaluation demonstrates that OpenDT not only reproduces prior findings but also extends energy-efficiency analysis capabilities, significantly reducing the mean absolute percentage error (MAPE) from 7.86% to 4.39%.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Calibration & Uncertainty Quantification
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Datacenters are the backbone of our digital society, but raise numerous operational challenges. We envision digital twins becoming primary instruments in datacenter operations, continuously and autonomously helping with major operational decisions and with adapting ICT infrastructure, live, with a human-in-the-loop. Although fields such as aviation and autonomous driving successfully employ digital twins, an open-source digital twin for datacenters has not been demonstrated to the community. Addressing this challenge, we design, implement, and experiment using OpenDT, an Open-source, Digital Twin for monitoring and operating datacenters through a continuous integration cycle that includes: (1) live and continuous telemetry data; (2) discrete-event simulation using live telemetry from the physical ICT, with self-calibration; and (3) SLO-aware and human-approved feedback to physical ICT. Through trace-driven experiments with a prototype mainly covering stages 1 and 2 of the cycle, we show that (i) OpenDT can be used to reproduce peer-reviewed experiments and extend the analysis with performance and energy-efficiency results; (ii) OpenDT's online re-calibration can increase digital-twinning accuracy, quantified to a MAPE of 4.39% vs. 7.86% in peer-reviewed work. OpenDT adheres to FAIR/FOSS principles and is available at: https://github.com/atlarge-research/opendt/tree/hcp.

Problem

• datacenter
• digital twin
• open-source
• operational challenges
• sustainability

Innovation

• Digital Twin
• Self-Calibration
• Datacenter Operations
• Open-Source
• Discrete-Event Simulation