Data and Resource Management Challenges for Digital Twins

Digital twins are revolutionizing industries by providing real-time simulation, monitoring, and predictive analytics capabilities. However, their success hinges on overcoming significant data and resource management challenges. This keynote will explore four key issues critical to the advancement and scalability of digital twins. First, we will discuss the complexities of real-time data processing within the edge-cloud-IoT continuum, emphasizing the need for seamless integration and efficient resource allocation across distributed systems. Second, we will explore the use of Large Language Models (LLMs) for dynamic verification of the resilience of digital twins, highlighting their potential to enhance adaptability and real-time decision-making. Third, we will examine end-to-end monitoring strategies to ensure data integrity, transparency, and reliability, enabling trust in automated decision processes. Finally, we will address the integration of emerging computational technologies, such as quantum accelerators (e.g., Quantum Brilliance) and neuromorphic chips (like Intel Loihi and BrainChip Akida), at the edge network to accelerate data processing and improve the responsiveness of digital twins. This talk will provide insights into how these advancements can be leveraged to develop robust, scalable, and intelligent digital twin ecosystems, driving innovation and efficiency in realworld applications.