Cloud computing has been a dominant computing paradigm for data analysis during the past decades with various data sources such as IoT devices and sensors. The ever-increasing data and workloads generated by Cyber-Physical-Social applications and systems are posing significant burdens on the capacity-limited Internet and uncontrollable service delay, leading to the necessity to move computational nodes from the cloud data centers closer to the data source, i.e., at the network edge, for reduced latency. To meet the delay-sensitive and context-aware service requirements, edge-cloud orchestrated architecture has emerged to leverage the distributed resources of heterogeneous devices/facilities at both cloud data center and network edge.

The intelligent computing, boosted by the widely-adopted machine (deep) learning technology, has triggered enormous attention and research activities. The integration of artificial intelligence (AI), computing and communication will be the main approach to empower the edge-cloud orchestrated services and systems. In this special session, we aim to highlight the cutting-edge research on combining the advantages of AI and advanced computing paradigm.

This is a special session of the 13th IEEE International Conference on Cyber, Physical and Social Computing (http://cse.stfx.ca/~cybermatics/2020/cpscom/index.php). Please submit your paper via the submission site (TBA) and select the special session of “Intelligent Computing for Edge-Cloud Orchestrated Applications and Systems” marked with “CPSComICEC”.

Accepted conference papers will be published by IEEE (IEEE-DL and EI indexed). Selected papers, after further extensions and revisions, will be recommended to journal special issues. More details at the conference website:
http://cse.stfx.ca/~cybermatics/2020/cpscom/index.php

Potential topics include but are not limited to:

•    Service placement in edge-cloud orchestrated applications and systems
•    Service composition and decomposition in edge-cloud orchestrated applications and
     systems
•    Service quality evaluation in edge-cloud orchestrated applications and systems
•    Performance modelling and optimization for edge-cloud orchestration
•    Communication protocol design for edge-cloud orchestrated applications and
     systems
•    Resource management in edge-cloud orchestrated systems
•    Security, privacy and trust in in edge-cloud orchestrated applications and systems
•    AI/ML algorithms for edge-cloud orchestrated applications and systems
•    System optimization for machine learning with edge-cloud orchestration
•    Machine learning, data mining and big data analytics with edge-cloud orchestration
•    Transfer learning and reinforcement learning with edge-cloud orchestration
•    Big data analytics frameworks with edge-cloud orchestration

Session Chairs：
Laizhong Cui, Shenzhen University (cuilz@szu.edu.cn)
Yipeng Zhou, Macquarie University (yipeng.zhou@mq.edu.au)