Program​​​

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08:50 - 09:00

Opening Session

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09:00 - 10:00

Keynote Session: Functional Data Analysis as a Foundational Framework for Modeling Time Varying Cloud Telemetry

Keynote Speaker: Shiwen Mao (Auburn University , USA)

​Session Chair: Jie Wu

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Abstract: Cloud computing underpins modern data infrastructure and continuously generates high frequency telemetry from IoT sensors, serverless functions, and virtualized resource logs. Similar time varying data streams arise across intelligent transportation systems, wireless sensing platforms, and connected device ecosystems. Such observations are inherently functional in nature, better modeled as smooth trajectories evolving over continuous domains rather than as isolated tabular records. Yet most analytics pipelines remain rooted in discrete machine learning models that overlook temporal continuity, cross trajectory dependence, and latent functional structure. Functional Data Analysis (FDA) provides a principled statistical framework for modeling data at the function level through smoothing, basis representations, and covariance driven dimensionality reduction. Despite its strong theoretical foundations, FDA remains underutilized in large scale computing and sensing systems due to gaps between statistical methodology and engineering deployment. This talk highlights FDA as a unifying modeling paradigm for time varying data, presenting recent work in traffic flow modeling, RF sensing, RFID signal analysis, and device fingerprinting. Across these domains, functional representations demonstrate improved robustness to noise and missing data, enhanced interpretability of temporal dynamics, and stronger cross domain generalization, positioning FDA as a scalable foundation for modern cyber physical analytics. 

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​Bio: Shiwen Mao is a Professor and Earle C. Williams Eminent Scholar and Director of the Wireless Engineering Research and Education Center at Auburn University, Auburn, AL, USA. Dr. Mao's research interest includes wireless networks, RF sensing and IoT, smart health, and machine learning. He is the editor-in-chief of IEEE Transactions on Cognitive Communications and Networking, an Associate Editor-in-Chief of IEEE Internet of Things Journal, a member-at-large on the Board of Governors of IEEE Communications Society, and Vice President of Technical Activities of IEEE Council on Radio Frequency Identification (CRFID). He was the General Chair of IEEE INFOCOM 2022, a TPC Chair of IEEE INFOCOM 2018, and a TPC Vice-Chair of IEEE GLOBECOM 2022. He is a co-recipient of several technical and service awards from the IEEE, and a Fellow of the IEEE in the Class of 2019.

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10:00 - 10:30

Coffee Break

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10:30 - 12:00

Session 1: Network Technology

​Session Chair: Yingchi Mao

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LiSFC-Search: Lifelong Search for Network SFC Optimization under Non-stationary Drifts

Zuyuan Zhang (George Washington University, USA); Vaneet Aggarwal (Purdue University, USA); Tian Lan (George Washington University, USA)

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Cadence: Scalable RDMA Queue Pair Multiplexing through Fine-grained WQE Scheduling

Dexuan Liao, Jiao Zhang, Yuxuan Hu, Kaitai Zhang, Xudong Li and Jiaxin Li (Beijing University of Posts and Telecommunications, China)
 

LNMAN: Enabling Lossless RDMA over Metropolitan Area Networks

Yuxian Pang and Qian Wang (China Telecommunications Corporation, China)

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Multi-Armed Bandit Autoscaling with Monotonic Arm Banning for Distributed NextG Services

Antonio Scarvaglieri, Fabio Busacca and Raoul Raftopoulos (University of Catania, Italy)

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12:00 - 14:00

Lunch Break

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14:00 - 15:30

Session 2: Cloud System

​Session Chair: Mengfei Zhu

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DPPLCF: A Proactive Least-Connection Load Balancing Mechanism for Cloud-Native Data Centers

Ting-Ching Hung, Chein Chen and Jyh-Cheng Chen (National Yang Ming Chiao Tung University, Taiwan)

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DLBF: A Dynamic Load Balancing Framework Resilient to Packet Reordering for RDMA

Xingjian Zhang, Jiaxue Liu and Yiran Zhang (Beijing University of Posts and Telecommunications, China); Hu Zhang (Qilu University of Technology, China); Shangguang Wang (Beijing University of Posts and Telecommunications, China)

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C-POD: An AWS Cloud Framework for Edge Pod Automation and Remote Wireless Testbed Sharing

Annoy Dey (University of Minnesota Twin Cities, USA); Vineet Sreeram (State University of New York at Buffalo, USA); Gokkul Eraivan Arutkani Aiyanathan and Maxwell E McManus (University at Buffalo, USA); Yuqing Cui (University of Minnesota - Twin Cities, USA); Guanying Sun (University at Buffalo, USA); Elizabeth Serena Bentley (AFRL, USA); Nicholas Mastronarde (University at Buffalo, USA); Zhangyu Guan (University of Minnesota - Twin Cities, USA)

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Enabling Memory-Disaggregated Cloud Infrastructure for LLMs: An Adaptive CXL-based KV Cache Scheduling Approach

Ke Wang (IEIT SYSTEMS Co. Ltd., China); Yaqiang Zhang (Inspur (Beijing) Electronic Information Industry Co., Ltd., China); Fei Gao (IEIT, China); Guangyuan Xu and Yaqian Zhao (Inspur (Beijing) Electronic Information Industry Co., Ltd., China)

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15:30 - 16:00

Coffee Break

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16:00 - 17:30

Session 3: Cloud and Network for AI

​Session Chair: Shen Gao​

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A Cloud-Edge Collaborative System for Efficient LLM Fine-Tuning with Backbone Activation Caching

​Yuchu Chen and Yingchi Mao (Hohai University, China); Si Chen (China Huaneng Group Co., China); Rongzhi Qi, Tianfu Pang and Zhenxiang Pan (Hohai University, China)

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Robust Prewarming Orchestration for Multi-Model Elastic Inference under Traffic Uncertainty

Mengfei Zhu and Rui Kang (Kyoto University, Japan); Tong Li (Renmin University of China, China)

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CLAIR: SLA-Aware Inference Routing in Converged Cloud-Network Systems

Mulei Ma, Qixuan Li, Tailiang Liu, Haibin Wang, Zeyun Du and Tian Huang (Hong Kong University of Science and Technology (Guangzhou), China); Chenyu Gong (The Hong Kong University of Science and Technology (Guangzhou), China); Yang Yang (Hong Kong University of Science and Technology (Guangzhou) & Shanghai Reserach Center for Wireless Communications, China)

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Low-cost residual network for deep image steganalysis

Yunpeng Fan, Xiujuan Wang and Shuaibing Lu (Beijing University of Technology, China)