Taesik Cheung

Dr. Taesik Cheung

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Research Focus & Metrics

Academic Career Length

13Years (2014-Present)

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

Representative Publications

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8 October 2024 in Applied Sciences
Verified
Journal Article
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IAInayat Ali
Inayat Ali
SHSeungwoo Hong
Seungwoo Hong
TCTaesik Cheung
Taesik Cheung
Managing queuing delays is crucial for maintaining Quality of Service (QoS) in real-time media communications. Customizing traditional routing protocols to meet specific QoS requirements—particularly in terms of minimizing delay and jitter for real-time media—can be both complex and time-intensive. Furthermore, these protocols often encounter challenges when adapted for vendor-specific hardware implementations. To address these issues, this paper leverages the programmable features of Software-Defined Networking (SDN) to simplify the process of achieving user-defined QoS, bypassing the limitations of traditional routing protocols. In this work, we propose a policy-based routing module that integrates with traditional routing protocols to ensure QoS for real-time media flows. QoS is achieved by rerouting the flow along a new low-latency path calculated by the proposed module when the queuing delay exceeds a certain threshold. The experimental results demonstrate that the proposed solution significantly enhances the performance of traditional routing protocols within an SDN framework, effectively reducing the average end-to-end delay by 80% and total packet loss by 73%, while also improving jitter and alleviating network congestion.

20 February 2023 in Electronics
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Journal Article
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JRJihye Ryu
Jihye Ryu
JKJuhyeok Kwon
Juhyeok Kwon
JRJeong-Dong Ryoo
Jeong-Dong Ryoo
TCTaesik Cheung
Taesik Cheung
JJJinoo Joung
Jinoo Joung
Adopting reinforcement learning in the network scheduling area is getting more attention than ever because of its flexibility in adapting to the dynamic changes of network traffic and network status. In this study, a timeslot scheduling algorithm for traffic, with similar requirements but different priorities, is designed using a double deep q-network (DDQN), a reinforcement learning algorithm. To evaluate the behavior of the DDQN agent, a reward function is defined based on the difference between the estimated delay and the deadline of packets transmitted at the timeslot, and on the priority of packets. The simulation showed that the designed scheduling algorithm performs better than existing algorithms, such as the strict priority (SP) or weighted round robin (WRR) scheduler, in the sense that more packets arrived within the deadline. By using the proposed DDQN-based scheduler, it is expected that autonomous network scheduling can be realized in upcoming frameworks, such as time-sensitive or deterministic networking.

5 December 2018 in IEEE Transactions on Communications
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Journal Article
Closed access
CCChoong-Hee Cho Cho
Choong-Hee Cho Cho
TCTaesik Cheung
Taesik Cheung
JRJeong-Dong Ryoo
Jeong-Dong Ryoo

1 June 2014
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Report
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HHH. van Helvoort
H. van Helvoort
ADA. D'ALessandro
A. D'ALessandro
TCTaesik Cheung
Taesik Cheung
EOE. Osborne
E. Osborne
This document describes alternate mechanisms to perform some of the functions of MPLS Transport Profile (MPLS-TP) linear protection defined in RFC 6378, and also defines additional mechanisms. The purpose of these alternate and additional mechanisms is to provide operator control and experience that more closely models the behavior of linear protection seen in other transport networks. This document also introduces capabilities and modes for linear protection. A capability is an individual behavior, and a mode is a particular combination of capabilities. Two modes are defined in this document: Protection State Coordination (PSC) mode and Automatic Protection Switching (APS) mode. This document describes the behavior of the PSC protocol including priority logic and state machine when all the capabilities associated with the APS mode are enabled.