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 - Present, 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 (*:Co-first)

HotStorage

Neural Cloud Storage: Innovative Cloud Storage Solution for Cold Video

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

In Proceedings of the 15th ACM Workshop on Hot Topics in Storage and File Systems Jul 2023

Abs PAPER

Cloud storage providers offer different pricing tiers based on the access frequency of stored data. This pricing plan offers cost benefits for videos that are accessed less than once per month. However, the stringent requirement falls short in addressing the large number of "cold" videos stored today. This paper proposes Neural Cloud Storage (NCS), a pioneering approach to address the problem by applying neural enhancement, specifically content-aware super-resolution (SR). According to our preliminary cost-benefit analysis, NCS can further save an annual 14% total cost of ownership (TCO) compared to the cheapest AWS storage service for cold video. By reducing the cost, it expands the cold video coverage (from 25% to 38%) that can benefit from the multi-tiered service. As deep learning and computational resources continue to advance, we believe that neural enhancement will revolutionize the field of cloud storage.
EuroSys

FlexPass: A Case for Flexible Credit-Based Transport for Datacenter Networks

Hwijoon Lim, Jaehong Kim, Inho Cho, Keon Jang, Wei Bai, and Dongsu Han

In Proceedings of the Eighteenth European Conference on Computer Systems Jul 2023

Abs PAPER Website

Proactive transports explicitly allocate bandwidth to each sender with credits which schedule packet transmission. While promising, existing proactive solutions share a stringent deployment requirement; they assume the perfect control of every link and packet in the network. However, the assumption breaks in practice because new transports are usually deployed gradually over time and legacy traffic always coexists. In this paper, we present FlexPass, a credit-based transport that takes deployment flexibility as a first-class citizen. FlexPass uses a novel combination of network and end-host designs to solve the problem of co-existence and gradual deployment. FlexPass leverages a proactive control loop to send credit-scheduled packets and a complementary reactive control loop to send unscheduled packets to utilize the spare bandwidth. Finally, FlexPass prevents queue buildups of both scheduled and unscheduled packets, and recovers lost packets efficiently. Our evaluation on the testbed shows that FlexPass maintains co-existence with legacy transports (DCTCP), while preserving the high-performance properties of the proactive transport. In large-scale simulations, we show that FlexPass delivers the best incremental benefits during the gradual deployment. We find traffic upgraded to FlexPass benefits from the bounded queue and reduced flow completion time by up to 44% compared to the legacy traffic, while minimizing the side-effect on the legacy flows.
CoNEXT

OutRAN: 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 Jul 2022 (Best Paper Award Nominee)

Abs PAPER PDF Website

Traffic from interactive applications demanding low latency has become dominant in cellular networks. However, existing schedulers of cellular network base stations fall short in delivering low latency when prior information (i.e., dedicated Quality of Service (QoS)) is unavailable; they become service agnostic and perform towards maximizing the radio resource utilization or user fairness. We identify a new opportunity of providing a better latency for those latency-sensitive traffic flows by additionally taking the Flow Completion Time (FCT) into account in downlink scheduling at the base stations. However, the key challenges are 1) it can bring a severe cost in optimization metrics of the existing scheduler and 2) it should work without prior knowledge of the traffic.To this end, we present OutRAN, a practical flow scheduler designed for Radio Access Network that co-optimizes the FCT and optimization objectives of the cellular scheduler. The resulting system does not require prior information. Through simulation and over-the-air evaluation, we demonstrate that OutRAN outperforms the legacy LTE/5G schedulers in FCT, which leads to the reduction in webpage load time of Android phones.
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 Jul 2022

Abs PAPER PDF Website

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 Annual Conference of the ACM Special Interest Group on Data Communication on the Applications, Technologies, Architectures, and Protocols for Computer Communication Jul 2020

Abs PAPER PDF Website

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 Proceedings of the 13th USENIX Conference on Operating Systems Design and Implementation Jul 2018

Abs PAPER PDF Website

Internet video streaming has experienced tremendous growth over the last few decades. However, the quality of existing video delivery critically depends on the bandwidth resource. Consequently, user quality of experience (QoE) suffers inevitably when network conditions become unfavorable. We present a new video delivery framework that utilizes client computation and recent advances in deep neural networks (DNNs) to reduce the dependency for delivering high-quality video. The use of DNNs enables us to enhance the video quality independent to the available bandwidth. We design a practical system that addresses several challenges, such as client heterogeneity, interaction with bitrate adaptation, and DNN transfer, in enabling the idea. Our evaluation using 3G and broadband network traces shows the proposed system outperforms the current state of the art, enhancing the average QoE by 43.08% using the same bandwidth budget or saving 17.13% of bandwidth while providing the same user QoE.