Kimoon Jeong

Dr. Kimoon Jeong

orcid

Research Focus & Metrics

Academic Career Length

8Years (2019-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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10 November 2019 in Lecture Notes in Computer Science
Verified
Book Chapter
Closed access
PDF file
WLWooHo Lee
WooHo Lee
BNBongNam Noh
BongNam Noh
YKYeonSu Kim
YeonSu Kim
KJKimoon Jeong
Kimoon Jeong

15 November 2025
Verified
Conference Paper
Open access
KPKyuli Park
Kyuli Park
SYSungmin Yoon
Sungmin Yoon
FTFarid Talibli
Farid Talibli
Sungyong ParkSungyong Park
Sungyong Park
JKJae-Hyuck Kwak
Jae-Hyuck Kwak
YKYoungjae Kim
Youngjae Kim

1 January 2026 in IEEE Internet of Things Journal
Verified
Journal Article
Closed access
Nosin Ibna MahbubNosin Ibna Mahbub
Nosin Ibna Mahbub
ASAfsana Kabir Sinthia
Afsana Kabir Sinthia
Eui-Nam HuhEui-Nam Huh
Eui-Nam Huh
THTran Hoang Hai
Tran Hoang Hai
KJKimoon Jeong
Kimoon Jeong

4 February 2026 in Applied Sciences
Verified
Journal Article
Open access
PDF file
Wan Yeon LeeWan Yeon Lee
Wan Yeon Lee
JRJinseung Ryu
Jinseung Ryu
SRSeungwoo Rho
Seungwoo Rho
SKSangwan Kim
Sangwan Kim
KJKimoon Jeong
Kimoon Jeong
Diskless computing nodes in cluster supercomputers require massive deployment procedure of operating systems software for their booting. Most previous studies for massive deployment of large files focus on developing flexible and scalable mechanisms in hidden or changeable network topologies, but the topology-optimal deployment has been rarely investigated in fixed open network topologies. In this article, we investigate the topology-optimal deployment method designed for a specific cluster supercomputer with the fat-tree topology. We examine possible deployment approaches and propose the optimal peer-to-peer deployment method. The proposed deployment method minimizes the number of peer-to-peer transmission steps by fully utilizing resources of the fat-tree topology and avoids contention between peer-to-peer communications. Simulation results show that the proposed method completes deployment at least nine times faster than the baseline method.