Kyungyong Lee is an associate professor in the College of Computer Science at Kookmin University. He is leading Distributed Data Processing Systems Lab. in the school. His current research topic covers big data platforms, large-scale distributed computing resource manangement, cloud computing, and peer-to-peer systems.

Education

He received the Ph. D. degree in the Department of Electrical and Computer Engineering at the University of Florida. At UF, he conducted research about efficient distributed computing resource management with his advisor Dr. Renato Figueiredo in the ACIS lab. Before joining UF, he received B.S. degree from Sungkyunkwan University.

Industry Experiences

Kyungyong has few industry experiecnes as a software development engineer and researcher.

• Amazon (Web Services). Seattle. WA. USA. (2014 ~ 2016) Software Development Engineer
  • In a team of building a next generation deep learning-based product recommendation platform for Amazon e-commerce site
  • In a team of building a scalable and fault-tolerant web server framework.
• Hewlett Packard (HP) Labs. Palo Alto. CA. USA (2012 ~ 2014) Research Associate Intern
  • distributedR: A package that enables large-scale machine learning using R.
  • Interference and priority-aware multiple parallel application scheduling
• Samsung Electronics. Suwon. Korea (2004 ~ 2008) Software Development Engineer
  • Developing UPNA/DLNA applications

Research

• ACE-AI : Autonomic Cloud Environment for AI
  • Autonomically providing cost and performance efficient cloud environment for deep learning training and inference
  • Enhancing the spare cloud instance usages to save cloud usage cost
  • A Software StarLab project (cloud computing) selected in 2022. project description in Korean
• ABC²: Autonomic BigData Cloud Computing
  • Build an autonomic cloud computing service that abstracts complex infrastructure configurations
  • Providing an optimal kernel layer to utilize in a wide range of big-data processing applications
• Using cloud computing service to build cost-efficient and scalable big-data analytics platform
  • DeepSpotCloud - Utilizing EC2 GPU spot instances to build cost-efficient deep learning framework
• Efficient and fault-tolerant big data analytics platform pipeline
  • Task scheduling considering device heterogeneity (many core devices and GPU) and data locality
  • Unified views of diverse processing frameworks
  • Version-control of experiments with diverse parameters

Publications

• K. Kim, and Kyungyong Lee, ‘Making Cloud Spot Instance Interruption Events Visible’, ACM THE WEB CONFERENCE, WWW 2024, pdf acm bib

• J. Lim, K. Kim, and Kyungyong Lee, ‘Workload-Aware Live Migratable Cloud Instance Detector’, The 24th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGrid 2024, pdf ieee bib

• Y. Hur, and Kyungyong Lee, ‘CNN Training Latency Prediction Using Hardware Metrics on Cloud GPUs’, The 24th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGrid 2024, pdf ieee bib

• M. Song, Y. Hur, and Kyungyong Lee, ‘When Serverless Computing Meets Different Degrees of Customization for DNN Inference’, Nineth International Workshop on Serverless Computing (WoSC) held with ACM/IFIP Middleware 2023, pdf acm bib

• K. Kim, S. Park, J. Hwang, H. Lee, S. Kang, and Kyungyong Lee, ‘Public Spot Instance Dataset Archive Service’, ACM The Web Conference, WWW 2023 (Demo). pdf acm bib

• Unho Choi, and Kyungyong Lee, ‘Dense or Sparse : Elastic SPMM Implementation for Optimal Big-Data Processing’, IEEE Transactions on Big Data, Accepted in April. 2023. pdf ieee bib demo github

• Jeongchul Kim, Myungjun Son, and Kyungyong Lee, ‘MPEC: Distributed Matrix Multiplication Performance Modeling on a Scale-out Cloud Environment for Data Mining Jobs’, IEEE Transactions on Cloud Computing, VOL 10, NO. 1, JANUARY-MARCH, 2022 pdf ieee bib demo

• S. Lee, J. Hwang, and Kyungyong Lee, ‘SpotLake: Diverse Spot Instance Dataset Archive Service’, IEEE International Symposium on Workload Characterization (IISWC) 2022, pdf ieee bib demo github

• S. Lee, Y. Heo, S. Park, and Kyungyong Lee, ‘PROFET : PROFiling-based CNN Training Latency ProphET for GPU Cloud Instances’, IEEE International Conference on Big Data, 2022, pdf ieee bib demo github

• J. Choi, S. Park, and Kyungyong Lee, ‘All-You-Can-Inference : Serverless DNN Model Inference Suite’, Eighth International Workshop on Serverless Computing held with ACM/IFIP Middleware 2022, pdf bib acm bib github

• Jungae Park, Unho Choi, Seungwoo Kum, Jaewon Moon, and Kyungyong Lee, ‘Accelerator-Aware Kubernetes Scheduler for DNN Tasks on Edge Computing Environment’, The Sixth ACM/IEEE Symposium on Edge Computing (SEC 2021 Poster session) pdf IEEE bib demo

• Jueon Park and Kyungyong Lee, ‘S-MPEC: Sparse Matrix Multiplication Performance Estimator on a Cloud Environment’, International Journal of Cluster Computing, Springer, 2021 pdf online springer bib

• Jungae Park, Hyunjune Kim, and Kyungyong Lee, ‘Evaluating Concurrent Executions of Multiple Function-as-a-Service Runtimes with MicroVM’, IEEE International Conference on Cloud Computing 2020 pdf ieee bib

• Jaeghang Choi and Kyungyong Lee, ‘Evaluation of Network File System as a Shared Data Storage in Serverless Computing’, Sixth International Workshop on Serverless Computing held with ACM/IFIP Middleware 2020 pdf acm bib

• Jueon Park and Kyungyong Lee, ‘Performance Prediction of Sparse Matrix Multiplication on a Distributed BigData Processing Environment’, 6th International Workshop on AMGCC held with IEEE ACSOS-C, 2020 pdf ieee bib

• Jeongchul Kim and Kyungyong Lee, ‘I/O Resource Isolation of Public Cloud Serverless Function Runtimes for Data-Intensive Applications’, International Journal of Cluster Computing, Springer, 2020 pdf online springer bib

• Jeongchul Kim, and Kyungyong Lee, ‘Practical Cloud Workloads for Serverless FaaS’, ACM Symposium on Cloud Computing - SoCC 2019 (Poster), 11/2019 pdf acm bib

• Jeongchul Kim, and Kyungyong Lee, ‘FunctionBench : A Suite of Workloads for Serverless Cloud Function Service’, IEEE International Conference on Cloud Computing 2019 (WIP paper), 07/2019 pdf bib ieee

• Sungjae Lee, Yeonji Lee, Junho Kim, Kyungyong Lee, ‘RnR: Extraction of Visual Attributes from Large-Scale Fashion Dataset’, Media Management Workshop held with IEEE International Conference on BigData 2019, 12, 2019 pdf ieee bib

• Jeongchul Kim, Jungae Park, and Kyungyong Lee, ‘Network Resource Isolation in Serverless Cloud Function Service’, IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W)2019, 06/2019 pdf bib ieee

• Myungjun Son, and Kyungyong Lee, ‘Distributed Matrix Multiplication Performance Estimator for Machine Learning Jobs in Cloud Computing’, IEEE International Conference on Cloud Computing 2018, 07/2018 pdf bib ieee

• Sarah Alkharif, Kyungyong Lee, and Hyeokman Kim, ‘LSTM Model to Forecast Time Series for EC2 Cloud Price’, The Fourth IEEE International Conference on Big Data Intelligence and Computing 2018, 08/2018 pdf bib ieee

• Kyungyong Lee, and Myungjun Son, ‘DeepSpotCloud: Leveraging Cross-Region GPU Spot Instances for Deep Learning’, IEEE International Conference on Cloud Computing 2017, 06/2017 pdf bib ieee

• Sarah Alkharif, Kyungyong Lee, and Hyeokman Kim, ‘Time-Series Analysis for Price Prediction of Opportunistic Cloud Computing Resources’, The International Conference on Emerging Databases, 08/2017 pdf bib springer

• Kyungyong Lee, Tae Woong Choi, Patrick Oscar Boykin, and Renato Figueiredo, ‘MatchTree: Flexible, Scalable, and Fault-tolerant Wide-area Resource Discovery with Distributed Matchmaking and Aggregation’, Future Generation Computer Systems - The International Journal of Grid Computing: Theory, Methods and Applications (FGCS) - Volume 29, Issue 6, August 2013. elsevier bib

• David I. Wolinsky, Panoat Chuchaisri, Kyungyong Lee, and Renato Figueiredo, ‘Experiences with Self-Organizing Decentralized Grids Using the Grid Appliance’, International Journal of Cluster Computing, 06/2013 springer bib

• Pierre St. Juste, Huengsik Eom, Kyungyong Lee, and Renato Figueiredo, ‘Enabling decentralized microblogging through P2PVPNs’, IEEE Consumer Communications and Networking Conference (CCNC), 2013, pdf bib ieee

• Kyungyong Lee and Renato Figueiredo, ‘MapReduce on Opportunistic Resources Leveraging Resource Availability’, IEEE CloudCom 2012, 12/2012 pdf bib ieee

• Kyungyong Lee, David I. Wolinsky, and Renato Figueiredo, ‘PonD : Dynamic Creation of HTC Pool on Demand Using a Decentralized Resource Discovery System’, In the 21st International ACM Symposium on High-Performance Parallel and Distributed Computing (HPDC 2012), 06/2012 pdf bib acm

• H. Zhao, Z. Yu, S. Tiwari, X. Mao, K. Lee, D. Wolinsky, X. Li and R. Figueiredo, ‘CloudBay: Enabling an Online Resource Market Place for Open Clouds’, In the 5th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2012), 11/2012 pdf bib acm

• Kyungyong Lee, Tae Woong Choi, Arijit Ganguly, David I. Wolinsky, Oscar Boykin, and Renato Figueiredo, ‘Parallel Processing Framework on a P2P System Using Map and Reduce Primitives’, In the 8th International Workshop on Hot Topics in Peer-to-Peer Systems in Conjunction with IPDPS 2011, 5/2011 pdf bib ieee

• David I Wolinsky, Kyungyong Lee, Oscar Boykin, and Renato Figueiredo, ‘On the Design of Autonomic, Decentralized VPNs’, In the 6th International Conference on Collaborative Computing (CollaborateCom 2010), 10/2010 pdf bib ieee

• Pierre St. Juste, David I Wolinsky, Kyungyong Lee, Oscar Boykin, and Renato Figueiredo, ‘SocialDNS: A Decentralized Naming Service for Collaborative P2P VPNs’, In the 6th International Conference on Collaborative Computing (CollaborateCom 2010), 10/2010 pdf bib ieee

Contact

Kyungyong Lee
College of Computer Science
Kookmin University
614 Dorm. B.
Jeongneung-ro 77.
Seongbuk-gu. Seoul. 02707
Map
phone: 82-2-910-4420

e-mail: I don'tleekySo@pleasekookminleave.meacalone.!kr