Research

We harness AI techniques (e.g., LLM, DNN, GNN) to improve the efficiency and resilience of datacenter systems. By learning from system traces, workload patterns, and network telemetry, our models enable intelligent scheduling, adaptive traffic control, and resource orchestration. This AI-driven approach addresses inefficiencies such as GPU underutilization and communication bottlenecks, paving the way for more sustainable and scalable infrastructure.

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We also innovate system and network architectures that are tailored to the unique demands of AI workloads. Large-scale model training introduces unpredictable computation–communication dynamics that strain conventional system software. Our research develops workload-aware scheduling policies, optimized GPU placement strategies, and high-performance network designs to accelerate distributed AI training and ensure efficient use of costly hardware resources.

Research highlight

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