Track Co-Chairs:
Omer Rana, Cardiff University, UK, ranaof@cardiff.ac.uk
Sidi Lu, William and Mary, USA, sidi@wm.edu
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. The Edge and Cloud Computing track aims to attract research that explores networking and computing management in the aforementioned computing continuum.
• Cloud computing
• 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
TPC Lists:
• Yanfu Zhang, William and Mary, USA
• Fei Dou, University of Georgia, USA
• Jin Lu, University of Georgia, USA
• Lu Zhang, Swansea University, UK
• Lanyu Xu, Oakland University, USA
• Qiang Liu, University of Nebraska-Lincoln, USA
• Wei Niu, University of Georgia, USA
• Zheng Song, University of Michigan-Dearborn, USA
• Changxin Bai, Kettering University, USA
• Yongtao Yao, University of Delaware, USA
• Xiaolong Ma, Clemson University, USA
• Haoxin Wang, Georgia State University, USA
• Nathaniel Hudson, University of Chicago, USA
• Shaleeza Sohail, The University of Newcastle, Australia
• Carlo Puliafito, University of Pisa, Italy
• Laurent D'Orazio, University of Rennes, France
• Yichen Luo, William & Mary, USA