As the digital landscape rapidly expands, data centers have become integral to the infrastructure of modern society, supporting everything from cloud computing to AI-driven applications. However, this growth has come with a significant environmental cost, making energy efficiency a critical concern. To address this challenge, we invite submissions to a special session dedicated to “Digital Twins for Energy-Efficient Data Centers,” focusing on innovative approaches and cutting-edge technologies that enhance sustainability and efficiency.

We encourage contributions from a wide range of disciplines, including but not limited to:

• Energy Efficiency and Sustainability: Strategies for reducing energy consumption and carbon footprints in data centers through digital twin technology.
• Predictive Maintenance: Utilizing digital twins for real-time monitoring and maintenance of data center infrastructure, minimizing downtime and operational costs.
• AI and Machine Learning Integration: Leveraging AI and ML to enhance the functionality and efficiency of digital twins in data center environments.
• IoT and Sensor Networks: Implementing IoT devices and sensor networks for detailed data acquisition and monitoring, enabling more precise control over energy use.
• Disruptive Technologies: New, groundbreaking technologies and methodologies that push the boundaries of what is possible with digital twins in data centers.
• Interdisciplinary Approaches: Collaborative efforts that bring together experts from various fields to tackle the complex challenges of energy efficiency in data centers.

Authors are invited to submit original research papers, case studies, or review articles that align with the session’s theme. Submissions should highlight the potential for practical implementation, scalability, and the interdisciplinary nature of the work.

Important Dates:

Paper abstracts: November 3, 2024, 23:59 AOE

Full paper submission: November 10, 2024, 23:59 AOE

Author notification: December 20, 2024

Camera ready full papers: January 27, 2025

• Lavinia Chiara Tagliabue (Associate Professor UnitTo)
• Silvia Meschini (Assistant professor, UniTo)
• Stefano Rinaldi (Associate Professor, UniBs)