The proliferation of Internet of Things (IoT), mobile, and cyber-physical devices is driving an increasing demand for distributed computing capabilities. While cloud computing provides virtually unlimited processing and storage resources, many emerging applications require low latency, context awareness, mobility support, data locality, and efficient use of network resources. Edge and fog computing address these requirements by bringing computation and storage closer to end devices and data sources.
The integration of end devices, edge and fog infrastructures, and cloud platforms is therefore evolving toward a seamless computing continuum, where applications and services can be dynamically deployed, orchestrated, and migrated according to heterogeneous performance, resource, energy, and reliability requirements. At the same time, advances in artificial intelligence, including distributed and federated learning, large language models (LLMs), and foundation models, are introducing new challenges and opportunities for computation offloading, resource allocation, collaborative inference and training, and intelligent infrastructure management.
The Edge and Cloud Computing track aims to attract original research on the architectures, mechanisms, and management techniques enabling efficient execution of applications and services across the device–edge–fog–cloud continuum. Particular emphasis is placed on resource orchestration, computation offloading, service placement and migration, distributed infrastructure management, and collaborative execution of emerging workloads across heterogeneous computing environments.
• Resource management and orchestration across the edge–fog–cloud continuum
• Joint management of networking, computing, and storage resources
• Computation offloading, service placement, and migration
• Edge/fog computing architectures, platforms, and middleware
• Cloud-native, serverless, containerized, and microservice-based edge computing
• Network virtualization, NFV, and resource slicing for edge–cloud systems
• QoS/QoE, latency, reliability, and performance management
• Monitoring, telemetry, and autonomic management of distributed infrastructures
• Edge caching and distributed storage
• Distributed and collaborative AI execution across edge and cloud
• DNN/LLM partitioning, inference offloading, and collaborative edge–cloud computing
• Energy-efficient and sustainable edge/cloud computing
• Federated and distributed learning across the edge–cloud continuum
• LLM-based and agentic AI systems across the edge–cloud continuum
Track Chairs:
- Marica Amadeo, University of Messina, Italy, marica.amadeo@unime.it
- Sukhpal Singh Gill, Queen Mary University of London, UK, s.s.gill@qmul.ac.uk
1. Giuseppe Ruggeri, University Mediterranea of Reggio Calabria, Italy, giuseppe.ruggeri@unirc.it
2. Salvatore Serrano, University of Messina, Italy, salvatore.serrano@unime.it
3. Giovanni Stanco, University of Naples "Federico II" , Italy, giovanni.stanco@unina.it
4. Raouane Dehimi, University Mediterranea of Reggio Calabria, Italy, raouane.dehimi@unirc.it
5. Claudio Marche, University of Cagliari, Italy, claudio.marche@unica.it
6. Changxin Bai, Kettering University, USA, cbai1@kettering.edu
7. G.N.V. RajaReddy, University of Saskatchewan, Canada, cre138@usask.ca
8. Lanyu Xu, Oakland University, USA, lxu@oakland.edu
9. Maximo Morales Cespedes, UC3M, Spain, mmcesped@ing.uc3m.es
10. Miguel Gutiérrez Gaitán, Pontificia Universidad Católica de Chile, Chile, miguel.gutierrez@uc.cl
11. Qiang Liu, University of Nebraska-Lincoln, USA, qiang.liu@unl.edu
12. Xin Zhang, Binghamton University, USA, xzhang99@binghamton.edu
13. Yasir Saleem, Aberystwyth University, UK, yss1@aber.ac.uk
14. Zheng Song, University of Michigan-Dearborn, USA, zhesong@umich.edu
15. Zhengrui Qin, Northwest Missouri State University, USA, zqin@nwmissouri.edu
16. Dolly Sapra, University of Amsterdam, The Netherlands, d.sapra@uva.nl
17. Daniel Corujo, University of Aveiro, Portugal, dcorujo@ua.pt
18. Aleksey Charapko, University of New Hampshire, USA, aleksey.charapko@unh.edu
19. Daniel Müller-Gritschneder, TU Wien, Vienna, Austria, daniel.mueller-gritschneder@tuwien.ac.at
20. Paolo Bellavista, University of Bologna, Italy, paolo.bellavista@unibo.it