Track Co-Chairs:
Rajkumar Buyya, University of Melbourne, Australia, rbuyya@unimelb.edu.au
Schahram Dustdar, TU Wien, Austria, dustdar@dsg.tuwien.ac.at
Huaming Wu, Tianjin University, China, whming@tju.edu.cn
Description:
The pervasiveness of personal embedded mobile devices is a common phenomenon nowadays, and with the widespread of the Internet of Things (IoT), an increasing number of connected devices are being deployed. The use of Cloud computing to support massive amounts of data generated and consumed by these devices has some limitations, such as increased latency and substantial network traffic, hampering support for a variety of applications that need low response times. This leads to the emergence of edge computing, where the data processing is moved closer to the devices, where it is actually generated. On the other hand, the cloud is important to handle larger applications demanding processing tasks with various data sources that could not be handled at the edge. Therefore, a combination of end mobile and embedded devices, edge processing devices and the cloud is needed to give support to a variety of applications with heterogeneous requirements. The infrastructure comprising devices, edge, and cloud composes a continuum of computing capacity that needs new management mechanisms and algorithms to support efficient execution of applications. As applications of Large Language Models (LLMs) gain popularity and demand on mobile devices, resource-limited mobile terminals face challenges in efficiently performing large-model inference tasks. LLMs introduce significant complexities in task offloading and resource allocation within edge-cloud computing. The Edge and Cloud Computing track aims to attract research that explores networking and computing management in the aforementioned computing continuum.
• Resource management and allocation in Edge-Fog-Cloud
• Resource allocation in Edge-Fog-Cloud
• Joint scheduling and optimization of networking and distributed computing resources
• Integration of NFV into the Edge-Fog-Cloud
• Edge/fog computing and network services
• Middleware for cloud/fog computing applications
• Resource slicing in the computing continuum
• Autonomic distributed service and network management
• Business models for the computing continuum
• QoS/QoE management for static and mobile applications
• Distributed infrastructure monitoring
• Machine learning and distributed learning for edge and cloud resource management
• Edge Intelligence: Management of distributed machine learning tasks
• Distributed learning deployment, management and applications
• Datacenter networking
• Caching into the Edge-Fog-Cloud
• DNN Partitioning and Offloading in Edge-Fog-Cloud
• Big AI Models with collaborative edge-cloud computing
• Large Language Models (LLMs) inference offloading in Edge-Fog-Cloud
• Training acceleration for LLMs in Edge-Fog-Cloud
• LLMs empowered autonomous edge intelligence
• Realtime Internet of Things (IoT) applications
TPC Lists:
• Laurent D'Orazio, Univ Rennes, CNRS, IRISA, France
• Zheng Song, University of Michigan-Dearborn, USA
• Chandrashekar Jatoth, National Institute of Technology Raipur, India
• Fei Dou, University of Georgia, USA
• Minxian Xu, Chinese Academy Science, China
• Sidi Lu, William and Mary, USA
• Nathaniel Hudson, University of Chicago, USA
• Huijun Tang, Hangzhou Dianzi University, China
• Omer Rana, Cardiff University, U.K.
• Lanyu Xu, Oakland University, USA
• Xiaofei Wang, Tianjin University, China
• Jin Lu, University of Georgia, USA
• Qiang Liu, University of Nebraska-Lincoln, USA
• Deze Zeng, China University of Geosciences, China
• Changxin Bai, Kettering University, USA
• Sanjeev Sondur, Oracle Corporation, USA
• Xiaokang Zhou, Shiga University, Japan
• Haoxin Wang, Georgia State University, USA
• Zhi Zhou, Sun Yat-sen University, China
• Xiaolong Xu, Nanjing University of Information Science and Technology, China
• Mohammad Goudarzi, Monash University, Australia
• Jing Bi, Beijing University of Technology, China
• Sukhpal Singh Gill, Queen Mary University of London, U.K.
• Haitao Yuan, Beihang University, China
• Saurabh Garg, University of Tasmania, Australia
• Yuan Wu, University of Macau, China
• Anwesha Mukherjee, Mahishadal Raj College, India