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

Publications

[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