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PLATONT: Learning a Platonic Representation for Unified Network Tomography
Chengze Du, Heng Xu, Zhiwei Yu +2
Network tomography aims to infer hidden network states, such as link performance, traffic load, and topology, from external observations. Most existing methods solve these problems…
RailS: Load Balancing for All-to-All Communication in Distributed Mixture-of-Experts Training
Heng Xu, Zhiwei Yu, Chengze Du +5
Training Mixture-of-Experts (MoE) models introduces sparse and highly imbalanced all-to-all communication that dominates iteration time. Conventional load-balancing methods fail to…
RoTO: Robust Topology Obfuscation Against Tomography Inference Attacks
Chengze Du, Heng Xu, Zhiwei Yu +3
Tomography inference attacks aim to reconstruct network topology by analyzing end-to-end probe delays. Existing defenses mitigate these attacks by manipulating probe delays to misl…
Temporal-Aware GPU Resource Allocation for Distributed LLM Inference via Reinforcement Learning
Chengze Du, Zhiwei Yu, Heng Xu +3
The rapid growth of large language model (LLM) services imposes increasing demands on distributed GPU inference infrastructure. Most existing scheduling systems follow a reactive p…
FreCT: Frequency-augmented Convolutional Transformer for Robust Time Series Anomaly Detection
Wenxin Zhang, Ding Xu, Guangzhen Yao +6
Time series anomaly detection is critical for system monitoring and risk identification, across various domains, such as finance and healthcare. However, for most reconstruction-ba…