Urgent Computing Across the Device-Edge-Cloud Continuum
Description
This book explores how the Computing Continuum - spanning edge devices, cloud infrastructures, and high-performance computing systems - can empower a new generation of urgent analytics applications. Through cutting-edge scientific contributions and real-world case studies, it examines how dynamic, data-driven workflows can seamlessly integrate heterogeneous data sources, AI/ML models, and distributed computational resources to react to rapidly evolving situations and support time-critical decision-making across diverse application domains.
At the core of this book lies a bold vision: to enable the ephemeral and intelligent composition of data, services, and computational resources based on urgency, cost, energy constraints, and the ever-changing context of the environment. The chapters delve into the fundamentals of Urgent Science and the Computing Continuum, explore platforms for natural hazards and critical infrastructure resilience, and present real-life case studies on earthquake, public health, and emergency department triage. The book concludes with future directions in interactive and urgent HPC.
Bridging systems research, distributed AI, and domain sciences, this volume serves as both a reference and an inspiration for researchers, practitioners, and innovators seeking to shape the future of responsive, intelligent computing. By bringing together advances at the intersection of edge computing, cloud systems, HPC, and urgent analytics, the book offers a compelling perspective on how next-generation infrastructures can transform the way society responds to complex, time-sensitive challenges.
More details
Persons
Daniel Balouek is a Research Scientist at Inria, the French National Research Institute for Digital Science and Technology. His research focuses on distributed computing and complex applications, with a special emphasis on next-generation Utility Computing Infrastructures-encompassing Cloud, Edge, and beyond-as well as Urgent Science. He has authored over 100 peer-reviewed papers in journals and conferences, and is a co-recipient of an Outstanding Paper Award in Artificial Intelligence for Social Impact at the AAAI Conference on Artificial Intelligence. He holds a PhD in Computer Science from École Normale Supérieure de Lyon, France. His career has spanned institutions in industry and academia across France, the USA, Japan, and India.
Manish Parashar is the inaugural chief artificial intelligence officer at the University of Utah. He is also the executive director of the university's Scientific Computing and Imaging Institute-home to the $100 million One-U Responsible AI Initiative-and a presidential professor in the Kahlert School of Computing. Parashar is a leader in high-performance parallel and distributed computing, large-scale data management, and cyberinfrastructure. His efforts have enabled new insights across multiple scientific domains and earned him several honors, including the Institute of Electrical and Electronics Engineers (IEEE) Computer Society Sidney Fernbach Award and distinguished service awards from the Association for Computing Machinery (ACM) and the Computing Research Association. He is a fellow of the American Association for the Advancement of Science, ACM, and IEEE. Read more at
Content
Chapter 1 Urgent Computing: From raw data to decision-making under time and quality constraints.- Chapter 2 Engineering the Computing Continuum for scalable monitoring and applied feedback.- Chapter 3 The SAGE Platform: AI at the Edge for environmental monitoring.- Chapter 4 The TEMA Project: AI-based Architecture for Natural Disaster Management.- Chapter 5 The NDP platform: Open Data, Equitable Access and AI Services for All.- Chapter 6 Tradeoffs and Decision-making under uncertainty for Cyber-Physical Systems.-Chapter 7 Framework for evolution and online experimentation of Urgent Analytics.- Chapter 8 Application of federated learning in Continuum systems and constrained environments.- Chapter 9 Integrated Performance Framework for Fire Science and Management Workflows.- Chapter 10 A Distributed Multi-Sensor Machine Learning Approach to Earthquake Early Warning.- Chapter 11 Leveraging ICTs for Effective Pandemics Management.- Chapter 12 Enabling fluid ecosystems for urgent applications through next generation computing and swarm intelligence.