AI Data Center Networks: Traffic Characteristics, Technical Challenges, and System Optimization

Yang Xu Professor, Fudan University Talk Title: AI Data Center Networks: Traffic Characteristics, Technical Challenges, and System Optimization Abstract: With the rapid growth in the scale of large-model training and inference, AI data center networks are facing new traffic patterns and performance challenges that differ significantly from those in traditional data centers. AI workloads typically demand both high bandwidth and low latency, while their communication patterns exhibit strong periodicity, high burstiness, and phase-level synchronization. Meanwhile, multiple types of traffic—including distributed training, online inference, storage access, and parameter synchronization—often coexist within the same network. Their interactions can lead to network congestion, traffic imbalance, and performance interference, ultimately reducing GPU utilization, increasing job completion time, and degrading the quality of online services. To address these challenges, AI data center networks must evolve beyond general-purpose traffic forwarding toward specialized designs that account for AI communication patterns and workload-specific performance objectives. This talk will summarize the key traffic characteristics and technical challenges of AI data center networks. It will also present our recent research on traffic prediction, buffer management, traffic isolation, and traffic scheduling. Finally, the talk will discuss how to build next-generation AI data center networks that deliver high throughput, low latency, predictable performance, and strong isolation, thereby providing efficient and scalable network support for large-scale AI systems. Speaker Bio: Yang Xu is the Yaoshihua Chair Professor in the College of Computing Science and Artificial Intelligence at Fudan University. He received his Ph.D. from Tsinghua University in 2007. His research interests include AI systems, data center networks, and programmable networks. He has published more than 150 papers in leading international conferences and journals, including SIGCOMM, NSDI, SIGMETRICS, EuroSys, TON, JSAC, INFOCOM, ICNP, and CoNEXT. He also holds more than 10 U.S. and international patents in various areas of networking and computing. He served as TPC Co-Chair of IWQoS 2026, General Co-Chair of APNET 2025 and Vice General Co-Chair of APNET 2024, and has been a TPC member for numerous international conferences. He is an Editor for JNCA, and has served as a Guest Editor for JSAC, SCN, and Science China Information Sciences. His work has received several recognitions, including the Best Paper Award at ACM CoNEXT 2022 and Best Paper Nominee honors at ACM ICPP 2023 and IWQoS 2024.