JE Park

HJ Kim

CW Lee

SS Son

JC Kim

MJ Son

SJ Lee

UH Choi

JG Choi

SB Park

V. Mircheva

YS Hur

KH Kim

JH Lim

SH Kang

SK Cheon

TY Jin

Kyungyong Lee is an associate professor in the Department of Data Science at Hanyang University, leading the Distributed Data Processing Systems Lab. His current research topics include cloud computing, AI/big data platforms, and large-scale distributed computing resource management.

이경용 교수는 한양대학교 데이터사이언스학과 부교수로, 분산 데이터 처리 시스템 연구실을 이끌고 있습니다. 그의 현재 연구 주제는 클라우드 컴퓨팅, AI/빅데이터 플랫폼, 대규모 분산 컴퓨팅 자원 관리입니다.

KubePACS: Kubernetes Cluster Using Performant, Highly Available, and Cost Efficient Spot Instances

Pygration : Workload-Aware Live Migratable Cloud Instance Detector for Python Runtime

Orchestrating WASM-based MCP Tool Runtimes for AI Agents across Edge-Cloud Continuum

Why Do AI Agents Systematically Fail at Cloud Root Cause Analysis?

Multi-Node Spot Instances Availability Score Collection System

HybridServe: Adaptive WebAssembly-Container Runtime Selection for Edge Serverless Computing

Making Cloud Spot Instance Interruption Events Visible

Workload-Aware Live Migratable Cloud Instance Detector

CNN Training Latency Prediction Using Hardware Metrics on Cloud GPUs

When Serverless Computing Meets Different Degrees of Customization for DNN Inference

Public Spot Instance Dataset Archive Service

Dense or Sparse : Elastic SPMM Implementation for Optimal Big-Data Processing

MPEC: Distributed Matrix Multiplication Performance Modeling on a Scale-out Cloud Environment for Data Mining Jobs

SpotLake: Diverse Spot Instance Dataset Archive Service

PROFET : PROFiling-based CNN Training Latency ProphET for GPU Cloud Instances

All-You-Can-Inference : Serverless DNN Model Inference Suite

Accelerator-Aware Kubernetes Scheduler for DNN Tasks on Edge Computing Environment

S-MPEC: Sparse Matrix Multiplication Performance Estimator on a Cloud Environment

Evaluating Concurrent Executions of Multiple Function-as-a-Service Runtimes with MicroVM

Evaluation of Network File System as a Shared Data Storage in Serverless Computing

Performance Prediction of Sparse Matrix Multiplication on a Distributed BigData Processing Environment

I/O Resource Isolation of Public Cloud Serverless Function Runtimes for Data-Intensive Applications

Practical Cloud Workloads for Serverless FaaS

FunctionBench : A Suite of Workloads for Serverless Cloud Function Service

RnR: Extraction of Visual Attributes from Large-Scale Fashion Dataset

Network Resource Isolation in Serverless Cloud Function Service

Distributed Matrix Multiplication Performance Estimator for Machine Learning Jobs in Cloud Computing

LSTM Model to Forecast Time Series for EC2 Cloud Price

DeepSpotCloud: Leveraging Cross-Region GPU Spot Instances for Deep Learning

Time-Series Analysis for Price Prediction of Opportunistic Cloud Computing Resources

MatchTree: Flexible, Scalable, and Fault-tolerant Wide-area Resource Discovery with Distributed Matchmaking and Aggregation

Experiences with Self-Organizing Decentralized Grids Using the Grid Appliance

Enabling decentralized microblogging through P2PVPNs

MapReduce on Opportunistic Resources Leveraging Resource Availability

PonD : Dynamic Creation of HTC Pool on Demand Using a Decentralized Resource Discovery System

CloudBay: Enabling an Online Resource Market Place for Open Clouds

Parallel Processing Framework on a P2P System Using Map and Reduce Primitives

On the Design of Autonomic, Decentralized VPNs

SocialDNS: A Decentralized Naming Service for Collaborative P2P VPNs

Kyungyong Lee

Associate Professor

Jungae Park

Ph.D. Student

Jaeil Hwang

Moohyun Song

Kyumin Kim

M.S. Student

Seungwoo Jeong

Taeyoon Kim

Woohyeok Park

Juhyun Park

Hayoung Kim

Kyoohyun Lee

Master of Science. Student, Department of Artificial Intelligence

Soohyuk Lee

Undergraduate Researcher

Hyundo Jung

Undergraduate Student

Yeonguk Jang

Research Intern

Jingyu Lee

YuJin Jung

Distributed Data Processing Systems Lab

교수님 소개

이경용

연구실 소개

연구실 키워드

알럼나이가 간 회사

연구실 지원 방법

대우 조건

연도별 논문 인용수

저널 & 학회 논문 출판 수

연구실 논문 목록

KubePACS: Kubernetes Cluster Using Performant, Highly Available, and Cost Efficient Spot Instances

Pygration : Workload-Aware Live Migratable Cloud Instance Detector for Python Runtime

Orchestrating WASM-based MCP Tool Runtimes for AI Agents across Edge-Cloud Continuum

연구실 보유 특허

대학별 입시끈

우리 연구실 모집

교수님 소개

이경용

연구실 키워드

알럼나이가 간 회사

연구실 지원 방법

대우 조건

연도별 논문 인용수

저널 & 학회 논문 출판 수

연구실 논문 목록

KubePACS: Kubernetes Cluster Using Performant, Highly Available, and Cost Efficient Spot Instances

Pygration : Workload-Aware Live Migratable Cloud Instance Detector for Python Runtime

Orchestrating WASM-based MCP Tool Runtimes for AI Agents across Edge-Cloud Continuum

연구실 보유 특허

최근 3년간 연구실적 높은 연구실

대학별 입시끈

우리 연구실 모집