추민솔
^{Minsol Choo}

Email

cmss1217 at ajou.ac.kr

Research Interests

Distributed System, ML/AI

Introduction

Minsol Choo is a M.S. at the Department of Artificial Intelligence, Ajou University. He received the B.S. in the Department of Software, Ajou University, in 2018.

Publications

2026

정혁준,; 추민솔,; 최지헌,; 오상윤,

LLM 서빙 시스템의 요청 유형 간 자원 경합 분석과 유형 기반 스케줄링 Conference

2026년도 한국정보과학회 한국컴퓨터종합학술대회 (KCC 2026), 2026.

BibTeX

Choo, Minsol; Oh, Sangyoon

GASched: Goal-Adaptive Hierarchical Reinforcement Learning for Multi-Objective HPC Job Scheduling Conference

The 26th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2026), 2026.

Abstract | Links | BibTeX

@conference{Choo2026GASched,

title = {GASched: Goal-Adaptive Hierarchical Reinforcement Learning for Multi-Objective HPC Job Scheduling},

author = {Minsol Choo and Sangyoon Oh},

url = {https://ieeexplore.ieee.org/abstract/document/11619039?casa_token=I12UxdEC6WwAAAAA:6uXEo8rxEcsMx4E-92x3dbWIBvbsmM0ihAHoqeFzNmrPqSQTpj4tCiMlm7Hlk_D9AwMnmE5x3P6Z},

year  = {2026},

date = {2026-05-21},

urldate = {2026-05-21},

booktitle = {The 26th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2026)},

abstract = {High-performance computing (HPC) job scheduling is fundamentally a multi-objective problem because schedulers must simultaneously optimize multiple objectives, such as average slowdown and risk of starvation, facing trade-offs between them. However, which objective matters most can change significantly across workloads and operating conditions, and ignoring these shifts can lead to overall performance degradation. Therefore, understanding and managing the priorities among objectives is a crucial issue for real-world HPC schedulers. However, prior works either target a single objective or treat multi-objective optimization without explicitly capturing their priorities. This oversimplifies the decision problem that real-world schedulers actually face. To address this issue, we present GASched, a Goal-Adaptive HPC Job Scheduler based on hierarchical reinforcement learning (HRL). GASched models whose objectives should be emphasized and allow these priorities to change as the system evolves. A high-level agent selects a subset of objectives to prioritize based on the current queue and cluster status. A lowlevel agent then observes both the current system state and chosen objectives and follows the goal by making scheduling decisions. By repeatedly updating the high-level choices based on long-term feedback, GASched can automatically transfer its focus without manual re-tuning. Through experiments using a diverse set of trace-driven workloads, we demonstrate that GASched balances trade-offs across several key objectives while reducing tail waiting time by up to 93.22% and mean waiting time by up to 89.75% compared with all baseline methods.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}