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cs.DC2026
FATE: Future-State-Aware Scheduling for Heterogeneous LLM Workflows
Zirui Huang, Yi-Xiang Hu, Feng Wu +1
Large language model (LLM) applications are increasingly executed as heterogeneous multi-stage workflows rather than isolated inference calls. In these workflow directed acyclic gr…
cs.LG2026
LeJOT-AutoML: LLM-Driven Feature Engineering for Job Execution Time Prediction in Databricks Cost Optimization
Lizhi Ma, Yi-Xiang Hu, Yihui Ren +2
Databricks job orchestration systems (e.g., LeJOT) reduce cloud costs by selecting low-priced compute configurations while meeting latency and dependency constraints. Accurate exec…
cs.DC2026
iScheduler: Reinforcement Learning-Driven Continual Optimization for Large-Scale Resource Investment Problems
Yi-Xiang Hu, Yuke Wang, Feng Wu +3
Scheduling precedence-constrained tasks under shared renewable resources is critical to modern computing platforms. It is often modeled as the Resource Investment Problem (RIP) by…