Workshops
The 4th International Workshop on Intelligent Cloud Computing and Networking (ICCN 2024)
ICCN 2024 – Opening and Keynote Session 1: Building Resilient AI: Exploring Robustness and Heterogeneity in Federated Learning
Session Chair
Ruidong Li (Kanazawa University, Japan)
ICCN 2024 – Cloud and Edge Computing 1
AI-Driven Automation for Optimal Edge Cluster Network Management
Cheikh Saliou Mbacke Babou (National Institute of Information and Communications Technology (NICT), Japan); Yasunori Owada, Masugi Inoue, Kenichi Takizawa and Toshiaki Kuri (National Institute of Information and Communications Technology, Japan)
Speaker
Delay Analysis of Multi-Priority Computing Tasks in Alibaba Cluster Traces
Chenyu Gong (The Hong Kong University of Science and Technology (Guangzhou), China); Mulei Ma (Hong Kong University of Science and Technology (Guangzhou), China); Liekang Zeng (Hong Kong University of Science and Technology (Guangzhou) & Sun Yat-Sen University, China); Yang Yang (Hong Kong University of Science and Technology (Guangzhou), China); Xiaohu Ge (Huazhong University of Science & Technology, China); Liantao Wu (East China Normal University, China)
Speaker
Power Efficient Edge-Cloud Cooperation by Value-Sensitive Bayesian Attractor Model
Tatsuya Otoshi, Hideyuki Shimonishi, Tetsuya Shimokawa and Masayuki Murata (Osaka University, Japan)
Speaker
DRL-Based Two-Stage SFC Deployment Approach under Latency Constraints
Aleteng Tian (Beijing Jiaotong University, China); Bohao Feng (Beijing Jiaotong Unviersity, China); Yunxue Huang and Huachun Zhou (Beijing Jiaotong University, China); Shui Yu (University of Technology Sydney, Australia); Hongke Zhang (Beijing Jiaotong University, China)
Speaker
Session Chair
Zhi Zhou (Sun Yat-sen University, China)
ICCN 2024 – Cloud and Edge Computing 2
Sample-efficient Learning for Edge Resource Allocation and Pricing with BNN Approximators
Feridun Tütüncüoğlu and György Dán (KTH Royal Institute of Technology, Sweden)
Pricing and resource allocation in EC thus have to cope with stochastic workloads, on the one hand offering resources at a price that is attractive to WDs, one the other hand ensuring revenue to the edge operator.
In this paper, we formulate the strategic interaction between an edge operator and WDs as a Bayesian Stackelberg Markov game. We characterize the optimal strategy of the WDs that minimizes their costs. We then show that the operator's problem can be formulated as a Markov Decision Process and propose a model-based reinforcement learning approach, based on a novel approximation of the workload dynamics at the edge cell environment.
The proposed approximation leverages two Bayesian Neural Networks (BNNs) to facilitate efficient policy learning, and enables sample efficient transfer learning from simulated environments to a real edge environment. Our extensive simulation results demonstrate the superiority of our approach in terms of sample efficiency, outperforming state-of-the-art methods 30 times in terms of learning rate and by 50% in terms of operator revenue.
Speaker
Joint Optimization of Charging Time and Resource Allocation in Wireless Power Transfer Assisted Federated Learning
Jingjiao Wang (China Three Gorges University, China); Huan Zhou (Northwestern Polytechnical University, China); Liang Zhao, Deng Meng and Shouzhi Xu (China Three Gorges University, China)
results show that our proposed algorithm has better performance in terms of the total utility of all MDs compared with other benchmark methods.
Speaker
LLM-CloudSec: Large Language Model Empowered Automatic and Deep Vulnerability Analysis for Intelligent Clouds
Daipeng Cao and Jun Wu (Waseda University, Japan)
Speaker
Towards Space Intelligence: Adaptive Scheduling of Satellite-Ground Collaborative Model Inference with Space Edge Computing
Yuanming Wang (Sun Yat-Sen University, China); Kongyange Zhao, Xiaoxi Zhang and Xu Chen (Sun Yat-sen University, China)
Speaker
Session Chair
Jun Wu (Waseda University, Japan)
ICCN 2024 – Keynote Session 2: Collaborative Secure Edge Intelligence for 6G IoT
Session Chair
Ruidong Li (Kanazawa University, Japan)
ICCN 2024 – Cloud and Edge Security
Blockchain Meets O-RAN: A Decentralized Zero-Trust Framework for Secure and Resilient O-RAN in 6G and beyond
Zakaria Abou El Houda (University of Montreal, Canada); Hajar Moudoud (Universite de Sherbrooke, Canada); Lyes Khoukhi (ENSICAEN, Normandie University, France)
Speaker
Ciphertext-Only Attack on a Secure k-NN Computation on Cloud
Santosh Kumar Upadhyaya and Srinivas Vivek (International Institute of Information Technology, Bangalore, India); Shyam S M (Indian Institute of Science Bangalore, India)
Speaker
Deep Reinforcement Learning-based Trajectory Optimization and Resource Allocation for Secure UAV-Enabled MEC Networks
Gao Yuan (Tsinghua University, China); Yu Ding (Zhejiang University of Technology, China); Ye Wang (Lishui University, China); Weidang Lu (Zhejiang University of Technology, China); Yang Guo (Academy of Military Science of PLA, China); Ping Wang (Tsinghua University, China); Jiang Cao (Academy of Military Science of PLA, China)
Speaker Gao Yuan; Yu Ding; Weidang Lu
A Pragmatical Approach to Anomaly Detection Evaluation in Edge Cloud Systems
Sotiris Skaperas (University of Macedonia & ATHENA Research and Innovation Center, Greece); Georgios Koukis and Ioanna Angeliki Kapetanidou (Democritus University of Thrace & ATHENA Research and Innovation Center, Greece); Vassilis Tsaoussidis (Democritus University of Thrace, Greece); Lefteris Mamatas (University of Macedonia, Greece)
Speaker
ICCN 2024 – Cloud and Edge Applications
Latency and Bandwidth Benefits of Edge Computing for Scientific Applications
Abdur Rouf and Batyr Charyyev (University of Nevada Reno, USA); Engin Arslan (University of Texas Arlington, USA)
Speaker
Edge-assisted Super Resolution for Volumetric Video Enhancement
Jie Li (Northeast University, China); Di Xu, Zhiming Fan, Jinhua Wang and Xingwei Wang (Northeastern University, China)
Speaker
Double-Agent Deep Reinforcement Learning for Adaptive 360-Degree Video Streaming in Mobile Edge Computing Networks
Suzhi Bi, Haoguo Chen, Xian Li, Shuoyao Wang and Xiao-Hui Lin (Shenzhen University, China)
Speaker
Accurate Water Gauge Detection by Image Data Augmentation using Erase-Copy-Paste
Guorong Ye and Chen Chen (Xidian University, China); Yang Zhou (Ministry of Water Resources of China, China); Hao Wang (the Xi'an Molead Technology Co. LTD., China); Lei Liu and Qingqi Pei (Xidian University, China)
In this study, a data augmentation strategy named ECP is proposed to solve the shortage of water gauge images, which can extend the dataset by mixing water gauge images. We designed two experiments to verify the effectiveness of ECP, especially in practical hydrology industry projects. Numerical results show that our proposed ECP can promote average precision and average recall of representative object detection algorithms. In addition, compared with other mixed-image methods, the performance improvement of ECP is better. Our experimental results indicated that the ECP can increase the dataset diversity, and enhance the water gauge detection algorithm accuracy and generalization ability in practical industrial applications.
Speaker
Session Chair
Xian Li (Shenzhen University, China)
ICCN 2024 – Cloud and Edge Computing 3
Hierarchical Charging and Computation Scheduling for Connected Electric Vehicles via Safe Reinforcement Learning
Liang Li (Peng Cheng Laboratory, China); Lianming Xu, Xiaohu Liu, Li Wang and Aiguo Fei (Beijing University of Posts and Telecommunications, China)
Speaker
Multi-Aspect Edge Device Association Based on Time-Series Dynamic Interaction Networks
Xiaoteng Yang, Jie Feng, Xifei Song and Feng Xu (Xidian University, China); Yuan Liu (Guangzhou University & Guangzhou, China); Qingqi Pei (Xidian University, China); Celimuge Wu (The University of Electro-Communications, Japan)
Speaker
Self-Interested Load Announcement by Edge Servers: Overreport or Underreport?
Chen Huang (Southern University of Science and Technology, China); Zhiyuan Wang (Beihang University, China); Ming Tang (Southern University of Science and Technology, China)
Speaker
Flow Size Prediction with Short Time Gaps
Seyed Morteza Hosseini, Sogand Sadrhaghighi and Majid Ghaderi (University of Calgary, Canada)
Speaker Seyed Morteza Hosseini
Session Chair
Shengyuan Ye (Sun Yat-Sen University, China)
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