CNC 2025
International Workshop on
Cloud-Network Convergence
In Conjunction with The 33rd IEEE International Conference on Network Protocols (ICNP 2025)
September 22, 2025
Seoul, South Korea
About CNC 2025
The Cloud-Network Convergence (CNC) is a critical step towards realizing the full potential of the Artificial Intelligence (AI), Internet of Things (IoT), edge computing, and the next wave of digital transformation. The core of CNC is to develop a framework that enables seamless interaction between cloud services and networks, facilitating faster and more reliable access to resources. The proposed initiative will address the challenges of adapting cloud architectures to support the unique requirements of network technologies. It will also explore the potential for network innovations to revolutionize cloud service delivery, emphasizing low latency, high bandwidth, and ubiquitous coverage. Key areas of focus will include network resource optimization, protocols for cloud access, and scalable solutions to support the growing demand for cloud-connected applications and services.
Committees
Workshop Chairs
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Jie Wu, Temple University, USA
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Eyuphan Bulut, Virgina Commonwealth University, USA
Technical Program Committee
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Elisa Rojas, University of Alcala, Spain
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Fernando Ramos, University of Lisbon, Portugal
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Imad Jawhar, AL Maaref University, Lebanon
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Jiliang Wang, Tsinghua University, China
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Kang Zhang, China Telecommunications, China
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Lars Dittmann, Technical University of Denmark, Denmark
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Majid Ghaderi, University of Calgary, Canada
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Marcelo Carvalho, Taxes State University, United States
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Murat Yuksel, University of Central Florida, United States
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Qiong Sun, China Telecommunications, China
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Quan Chen, Shanghai Jiao Tong University, China
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Theofanis Raptis, ITT-CNR, Italy
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Wei Chang, Saint Joseph University, United States
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Xiao Chen, Taxes State University, United States
Web and Submission Chair
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Shen Gao, China Telecommuncations, China
Topics
Original, unpublished contributions are solicited in all aspects of Cloud-Network Convergence from theory to systems and applications. Topics of interest include, but are not limited to:
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Scheduling and orchestration of cloud and network resource
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Operation of cloud and network systems
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Computing Power Network (CPN)
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Data Center Network and SD-WAN
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Convergence of IoT network, wireless network and optical network
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Network protocols, including SRv6 etc.
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Edge computing for network services
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Cloud native for network services
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AI/ML for Cloud-Network Convergence
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Cloud-Network Convergence for AI/ML
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Security of Cloud-Network Convergence
Paper Submission
All submissions should be written in English with a maximum length of 6 single-spaced, double-column pages using 10pt fonts on 8.5 x 11 inch paper, including all figures, tables, and references, in PDF format. The IEEE template available here.
Please submit your paper using workshop's HotCRP link .
Important Dates
Abstract Registration: July 11, 2025 (AoE)
Paper Submission: July 11, 2025 (AoE)
Notification of Acceptance: August 11, 2025 (AoE)
Camera Ready Version: August 18, 2025 (AoE)
Program
9:00 - 9:05 Opening Session
9:05 - 10:20 Session 1
Session Chair: Jie Wu
9:05 - 9:30
Title: Proactive Fault-tolerance Driven Task Scheduling System for IoV Edge Networks
Authors: Yaqiang Zhang, Rengang Li, Yaqian Zhao, Hongzhi Shi, Fei Gao, Xiaolin Chen, Xiao Li, Guangyuan Xu, Ke Wang
Duration: 20min for presentation and 5min for questions
9:30 - 9:55
Title: Latency-Aware Transformer Partitioning for Heterogeneous Edge Inference
Authors: Xiaoyao Huang
Duration: 20min for presentation and 5min for questions
9:55 - 10:20
Title: Enterprise Threat Detection with Explainable AI for Cloud-Network Convergence
Authors: Xingkai Wang, Fudi Wu, Xingwang Huang, Qiefu Wuri, Dujuan Gu, Runzi Zhang, Shen Gao
Duration: 20min for presentation and 5min for questions
10:20 - 10:45 Coffee Break
10:45 - 12:00 Session 2
Session Chair: Xiaoyao Huang
10:45 - 11:10
Title: Robust Mobile-Cloud Collaborative CNN Inference under Unreliable Wireless Networks
Authors: Sijia Li, Yumeng Liang, Jianjiang Li
Duration: 20min for presentation and 5min for questions
11:10 - 11:35
Title: Energy-Efficient Federated Learning via Dynamic DistillatioYi Dongn and Cloud-Network Collaboration
Authors: Yihan Chen, Haowen Xu, Yi Dong, Benteng Zhang, Xiaoming He, Miao Du, Yingchi Mao
Duration: 20min for presentation and 5min for questions
11:35 - 12:00
Title: Cloud Network Convergence Disaster Recovery Approach for Reliable Virtual Network Function
Authors: Shen Gao
Duration: 20min for presentation and 5min for questions
Contact
For more information, please send an email on CNC 2025 to jiewu@temple.edu and ebulut@vcu.edu.
Supporters
This workshop is supported by
