6 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…
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…
A Survey on the Applications of Generative Artificial Intelligence in Automated Driving Systems Test Scenario Generation Methods
Ji Zhou, Yongqi Zhao, Yixian Hu +4
Ensuring the safety and reliability of Automated Driving Systems (ADS) remains a critical challenge, as traditional verification methods such as large-scale on-road testing are pro…
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…
FFCG: Effective and Fast Family Column Generation for Solving Large-Scale Linear Program
Yi-Xiang Hu, Feng Wu, Shaoang Li +2
Column Generation (CG) is an effective and iterative algorithm to solve large-scale linear programs (LP). During each CG iteration, new columns are added to improve the solution of…