{"id":2640,"date":"2024-07-03T18:08:03","date_gmt":"2024-07-03T09:08:03","guid":{"rendered":"https:\/\/wise.ajou.ac.kr\/?page_id=2640"},"modified":"2025-09-09T16:27:32","modified_gmt":"2025-09-09T07:27:32","slug":"%ec%b6%94%eb%af%bc%ec%86%94","status":"publish","type":"page","link":"https:\/\/wise.ajou.ac.kr\/?page_id=2640","title":{"rendered":"\ucd94\ubbfc\uc194"},"content":{"rendered":"\t\t
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\ucd94\ubbfc\uc194
Minsol Choo<\/sup><\/h1>

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Email<\/h2>

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cmss1217 at ajou.ac.kr<\/p>

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Research Interests<\/h2>

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Distributed System, ML\/AI<\/p>

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Introduction<\/h2>

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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.<\/p>

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Publications<\/h2>

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2026<\/h3>
7.<\/div>

\uc815\ud601\uc900,; \ucd94\ubbfc\uc194,; \ucd5c\uc9c0\ud5cc,; \uc624\uc0c1\uc724,<\/p>

LLM \uc11c\ube59 \uc2dc\uc2a4\ud15c\uc758 \uc694\uccad \uc720\ud615 \uac04 \uc790\uc6d0 \uacbd\ud569 \ubd84\uc11d\uacfc \uc720\ud615 \uae30\ubc18 \uc2a4\ucf00\uc904\ub9c1 Conference<\/span> <\/p>

2026\ub144\ub3c4 \ud55c\uad6d\uc815\ubcf4\uacfc\ud559\ud68c \ud55c\uad6d\ucef4\ud4e8\ud130\uc885\ud569\ud559\uc220\ub300\ud68c (KCC 2026), <\/span>2026<\/span>.<\/p>

BibTeX<\/a><\/span><\/p>

@conference{hyeokjunkcc2026,
\r\ntitle = {LLM \uc11c\ube59 \uc2dc\uc2a4\ud15c\uc758 \uc694\uccad \uc720\ud615 \uac04 \uc790\uc6d0 \uacbd\ud569 \ubd84\uc11d\uacfc \uc720\ud615 \uae30\ubc18 \uc2a4\ucf00\uc904\ub9c1},
\r\nauthor = {\uc815\ud601\uc900 and \ucd94\ubbfc\uc194 and \ucd5c\uc9c0\ud5cc and \uc624\uc0c1\uc724 },
\r\nyear  = {2026},
\r\ndate = {2026-06-26},
\r\nurldate = {2026-06-24},
\r\nbooktitle = {2026\ub144\ub3c4 \ud55c\uad6d\uc815\ubcf4\uacfc\ud559\ud68c \ud55c\uad6d\ucef4\ud4e8\ud130\uc885\ud569\ud559\uc220\ub300\ud68c (KCC 2026)},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {conference}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div><\/div><\/div>
6.<\/div>
 Choo, Minsol;  Oh, Sangyoon<\/p>
GASched: Goal-Adaptive Hierarchical Reinforcement Learning for Multi-Objective HPC Job Scheduling<\/a> Conference<\/span> <\/p>
The 26th IEEE\/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2026), <\/span>2026<\/span>.<\/p>
Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
@conference{Choo2026GASched,
\r\ntitle = {GASched: Goal-Adaptive Hierarchical Reinforcement Learning for Multi-Objective HPC Job Scheduling},
\r\nauthor = {Minsol Choo and Sangyoon Oh},
\r\nurl = {https:\/\/ieeexplore.ieee.org\/abstract\/document\/11619039?casa_token=I12UxdEC6WwAAAAA:6uXEo8rxEcsMx4E-92x3dbWIBvbsmM0ihAHoqeFzNmrPqSQTpj4tCiMlm7Hlk_D9AwMnmE5x3P6Z},
\r\nyear  = {2026},
\r\ndate = {2026-05-21},
\r\nurldate = {2026-05-21},
\r\nbooktitle = {The 26th IEEE\/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2026)},
\r\nabstract = {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.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {conference}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
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.<\/div>
Close<\/a><\/p><\/div>