Jaehong Kim

jaehong950305@gmail.com | Google Scholar | LinkedIn | CV

Hi, I am a Ph.D. student in Intelligent Network Architecture Research Group at KAIST, advised by Prof. Dongsu Han.

My research interest lies in any computer systems or applications that are networked; Internet services, cloud, and systems for Artificial Intelligence. I focus on improving their performance to enhance the user experience with novel ideas.

I have worked on 1) applying neural enhancement to video streaming in a way that maximizes viewer experience and 2) designing a practical cell-scale scheduler for latency-sensitive traffic in 4G/5G networks.

Educations

Ph.D., School of Electrical Engineering, KAIST
- Feb 2020 - Feb 2024 (Expected), Advisor: Dongsu Han
M.S., School of Electrical Engineering, KAIST
- Aug 2018 - Feb 2020, Advisor: Dongsu Han
B.S., School of Electrical Engineering, KAIST
- March 2014 - Aug 2018, Cum Laude

Publications

CoNEXT

Co-optimizing for Flow Completion Time in Radio Access Network

Jaehong Kim, Yunheon Lee, Hwijoon Lim, Youngmok Jung, Song Min Kim, and Dongsu Han

In Proceedings of the 18th International Conference on emerging Networking EXperiments and Technologies (CoNEXT) (To appear) Dec 2022
SIGCOMM

NeuroScaler: Neural Video Enhancement at Scale

Hyunho Yeo, Hwijoon Lim, Jaehong Kim, Youngmok Jung, Juncheol Ye, and Dongsu Han

In Proceedings of the ACM SIGCOMM 2022 Conference Aug 2022

Abs HTML PDF Project Code

High-definition live streaming has experienced tremendous growth. However, the video quality of live video is often limited by the streamer’s uplink bandwidth. Recently, neural-enhanced live streaming has shown great promise in enhancing the video quality by running neural super-resolution at the ingest server. Despite its benefit, it is too expensive to be deployed at scale. To overcome the limitation, we present NeuroScaler, a framework that delivers efficient and scalable neural enhancement for live streams. First, to accelerate end-to-end neural enhancement, we propose novel algorithms that significantly reduce the overhead of video super-resolution, encoding, and GPU context switching. Second, to maximize the overall quality gain, we devise a resource scheduler that considers the unique characteristics of the neural-enhancing workload. Our evaluation on a public cloud shows NeuroScaler reduces the overall cost by 22.3\texttimes and 3.0–11.1\texttimes compared to the latest per-frame and selective neural-enhancing systems, respectively.
SIGCOMM

Neural-Enhanced Live Streaming: Improving Live Video Ingest via Online Learning

Jaehong Kim, Youngmok Jung, Hyunho Yeo, Juncheol Ye, and Dongsu Han

In Proceedings of the ACM SIGCOMM 2020 Conference Aug 2020

Abs HTML PDF Project Code

Live video accounts for a significant volume of today’s Internet video. Despite a large number of efforts to enhance user quality of experience (QoE) both at the ingest and distribution side of live video, the fundamental limitations are that streamer’s upstream bandwidth and computational capacity limit the quality of experience of thousands of viewers.To overcome this limitation, we design LiveNAS, a new live video ingest framework that enhances the origin stream’s quality by leveraging computation at ingest servers. Our ingest server applies neural super-resolution on the original stream, while imposing minimal overhead on ingest clients. LiveNAS employs online learning to maximize the quality gain and dynamically adjusts the resource use to the real-time quality improvement. LiveNAS delivers high-quality live streams up to 4K resolution, outperforming WebRTC by 1.96 dB on average in Peak-Signal-to-Noise-Ratio on real video streams and network traces, which leads to 12%-69% QoE improvement for live stream viewers.
OSDI

Neural Adaptive Content-aware Internet Video Delivery

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

In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18) Oct 2018

HTML PDF Project Code