System Software Lab.
KOREA UNIVERSITY
KOREA UNIVERSITY
고려대학교 컴퓨터학과
시스템 소프트웨어 연구실
We investigate the challenges of system software technologies with the goal of better performance and efficiency of computing infrastructures on all scales, from individual computers to massive cloud to planetary scale clusters.
Our research efforts cover exciting topics like system optimization techniques, resource efficiency, high-performance computing, and virtualization.
Two papers accepted at IEEE CLOUD'24 👏 [more]
🚀 Research project for consortium learning infrastructure, focusing on secure and high-performance AI training in private domains, is underway! 🙌🏼
🚀 Research project to develop end-to-end performance assurance technologies for 6G networks with leading universities and teams has been initiated! 📚
Hwiju has started her journey in the integrated MS-Ph.D. program. Also, three undergraduate interns, Joonhyuk, Youngjun, and Dohyeok, have joined our team! Welcome onboard! 😆
Five domestic conference papers accepted
Prof. Yang won "Excellent Lecture Award (우수강의상)" for the Operating Systems class in 2023-Spring.
Prof. Yang had an interview with the department as a new professor (KOR) [more]
Hwiju&Taeyun joined our team as undergrad. interns in September and November!
One paper accepted at IEEE Trans. on Services Computing [more]
One paper published at IEEE Trans. on Cloud Computing
Attended at ACM APNet'23 and Yeonho presented his paper with MSRA!
One paper accepted at CLOUD'23
One paper accepted at CCGrid'23
SSLab launched (Sep, 2023)
Predictive Placement of Geo-distributed Blockchain Nodes for Performance Guarantee, 2024 IEEE CLOUD
Harmonia: Accurate Federated Learning with All-Inclusive Dataset, 2024 IEEE CLOUD
Machine Learning-based Prediction Models for Control Traffic in SDN Systems, IEEE Transactions on Services Computing (2023)
Selective Preemption of Distributed Deep Learning Training, 2023 IEEE CLOUD
TeaVisor: Network Hypervisor for Bandwidth Isolation in SDN-NV, IEEE Transactions on Cloud Computing (2023)
Control Channel Isolation in SDN Virtualization: A Machine Learning Approach, 2023 IEEE/ACM 23rd CCGrid
Xonar: Profiling-based Job Orderer for Distributed Deep Learning, 2022 IEEE CLOUD
Prediction of the Resource Consumption of Distributed Deep Learning Systems, ACM SIGMETRICS 2022. / Proc. ACM Meas. Anal. Comput. Syst.
Bandwidth Isolation Guarantee for SDN Virtual Networks, 2021 IEEE INFOCOM
Network Monitoring for SDN Virtual Networks, 2020 IEEE INFOCOM