Youngmok Jung

Youngmok Jung

PhD student

About Me

My research interest focuses on redesigning systems with an AI approach.

Research Interest

Deep Learning, Next Generation Sequencing, CDN, and Distributed System


[2021 EuroSys] Towards Timeout-less Transport in Commodity Datacenter Networks

Acceptance rate 27/191: 14%

Hwijoon Lim, Wei Bai, Yibo Zhu, Youngmok Jung and Dongsu Han

Link to Website

[2020 MOBICOM] NEMO: Enabling Neural-enhanced Video Streaming on Commodity Mobile Devices

Acceptance rate 62/384: 16%

Hyunho Yeo, Chan Ju Chong, Youngmok Jung, Juncheol Ye and Dongsu Han

Link to Website

[2020 SIGCOMM] LiveNAS - Neural-Enhanced Live Streaming: Improving Live Video Ingest via Online Learning

Acceptance rate 53/250: 21%

Jaehong Kim, (CO-First)Youngmok Jung, Hyunho Yeo, Juncheol Ye and Dongsu Han

Link to Website

[2018 OSDI] NAS - Neural Adaptive Streaming

Acceptance rate 47/257: 18%

Hyunho Yeo, Youngmok Jung, Jaehong Kim, Jinwoo Shin, and Dongsu Han

Link to Website

Research Projects

Redesigning short-read alignment software with a machine-learning approach

Keywords: learned-index (Machine-learning), NGS, pattern matching

- Developed a short read alignment tool that is the first to leverage learned indices to find exact match of short substring (0-300 length) in the long reference DNA (6 billion length for human genome)
- Designed new Learned-index structure and replaced the traditional indexing methods used in alignment software
- Achieved up to 3.45x speedup in throughput over state-of-the-art algorithm by reducing the number of instructions by 4.60x, memory accesses by 8.77x, and LLC misses by 2.21x, while ensuring identical output.

Link to Github

Redesigning Live-video delivery system with a Deep-learning approach (SIGCOMM 2020)

Keywords: live-video delivery system, Deep-learning based image super-resolution

- LiveNAS system applies deep-learning based super-resolution on the live-video stream to enhance the original live-video stream
- Implemented new APIs in C++ WebRTC open-source code from Google; 1) to obtain decoded and encoded frames with the frame timestamp. 2) to control and obtain bandwidth information in the WebRTC.
- Developed the LiveNAS system which involves; 1) Online training and inferencing super-resolution DNN model during the live video stream. 2) Transmitting training data over RTP. 3) Controlling bandwidth allocation
- LiveNAS system delivers live video with the same quality as WebRTC using only 45.9% bandwidth on average or enhances average QoE by 69% compared to WebRTC using the same bandwidth.

Link to Website

Timeout-less Transport Protocol for Datacenter (EuroSys 2021)

Keywords: data center network, network protocol, network switching

- Implemented NS-3 simulation code[C++] for RDMA protocol and TLT.
- Managed switch configuration which involves priority-flow-control, Shared Buffer Pool size, and Packet Color.
- TLT protocol reduces 99%-ile FCT up to 91.7% on handling bursts of SET operations

Link to Github

Deep Learning-based Internet Video Delivery (OSDI 2018)

Keywords: Reinforcement-learning, video-on-demand content delivery system

- Developed an integrated-ABR (adaptive bit-rate) controller code[Python,Tensorflow] based on Reinforcement Learning (actor-critic framework of A3C).
- Developed simulation code for video delivery to train RL model and measure user QoE (Quality of Experience).
- Implemented evaluation code to run 5,832 hours of real-time video streaming in Google Cloud in just a few days.
- NAS system enhances the average QoE by 43.08% using the same bandwidth budget or saves 17.13% of bandwidth while providing the same user QoE.

Link to Website