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Distributed & Cloud Computing Lab
Distributed & Cloud Computing Lab
The Distributed & Cloud Computing LabD&C Lab, currently directed by professor Heonchang Yu, was established in 1998 as a one of the most pioneer laboratories in distributed computing areas located at Korea University. Over the past decades, D&C lab researchers have made valuable contribution in a wide range of distributed computing research such as mobile computing, fault tolerant systems, peer to peer computing, grid computing and cloud computing.
Currently, our lab serves as the focal point for research on Cloud Computing including data management, mobility management, fault tolerant and dependable services, overlay network construction, checkpoint, spot instance management of virtual machines, cost-efficient scheduling algorithms, replication of resources etc.
Minjae Kang (M.S. student) presented his paper “MOBOS: Co-optimizing Cost and Execution Time in Serverless Workflow with Multi-Objective Bayesian Optimization” at the IEEE International Conference on Cloud Computing 2025 (CLOUD 2025).
Hyunseung Jung (M.S. student) presented his paper “Korel: Dynamic AMP-based Straggler Mitigation in Multi-Tenant Distributed Deep Learning Environments” at the IEEE International Conference on Cloud Computing 2025 (CLOUD 2025).
Myeong Jun Kim (M.S. student) presented his paper “ReSACO: A Meta Reinforcement Learning Method for Fast Offloading in Mobile Edge Computing” at the IEEE International Conference on Cloud Computing 2025 (CLOUD 2025).
Hokun Park (Ph.D student) presented his paper “HEART: Heterogeneous-Aware Traffic Allocation in Multi-Replica Deployments on Kubernetes” at the IEEE International Conference on Cloud Computing 2025 (CLOUD 2025).
Jeonghui Lee (Undergraduate student) presented his paper “A Performance Analysis by Straggler Processing Techniques for DL frameworks that support real-time optimization in heterogeneous GPUcloud environments” at the Domestic Conference KCC 2025.
Seoeun Kwon (Undergraduate student) presented her paper “Dynamic Replica Replacement Technique Based on Deep Reinforcement Learning for Cost-Effective Cluster Utilization in Heterogeneous Kubernetes Environment” at the Domestic Conference KCC 2025.
Minjae Jung (M.S. student) presented his paper “Performance Analysis of Inference Using Different Batch Policies in TensorRT-LLM Environment” at the Domestic Conference KCC 2025.