7 papers
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…
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…
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…
LeJOT: An Intelligent Job Cost Orchestration Solution for Databricks Platform
Lizhi Ma, Yi-Xiang Hu, Yuke Wang +5
With the rapid advancements in big data technologies, the Databricks platform has become a cornerstone for enterprises and research institutions, offering high computational effici…
CLCR: Contrastive Learning-based Constraint Reordering for Efficient MILP Solving
Shuli Zeng, Mengjie Zhou, Sijia Zhang +3
Constraint ordering plays a critical role in the efficiency of Mixed-Integer Linear Programming (MILP) solvers, particularly for large-scale problems where poorly ordered constrain…
Beyond Local Selection: Global Cut Selection for Enhanced Mixed-Integer Programming
Shuli Zeng, Sijia Zhang, Shaoang Li +2
In mixed-integer programming (MIP) solvers, cutting planes are essential for Branch-and-Cut (B&C) algorithms as they reduce the search space and accelerate the solving process. Tra…