@article{sanghoyeojpdc202607,

title = {SpaceFlow: Runtime Enforcement of Cloud-Native GPU Spatial Partitioning},

author = {Sangho Yeo and Jiheon Kim and Sangyoon Oh },

url = {https://www.sciencedirect.com/science/article/pii/S0743731526000894?__cf_chl_f_tk=9290J7qEI.ciFqQ7qLcwHCGVUby21dj2aNeWnhHOCUg-1783310840-1.0.1.1-0gj8vTrE9ZmcMFuOTF8xbgHcjhwMUo2Zt7mEhGVfTmA},

doi = {https://doi.org/10.1016/j.jpdc.2026.105311},

year  = {2026},

date = {2026-07-02},

urldate = {2026-07-02},

journal = {Journal of Parallel and Distributed Computing},

abstract = {As GPU capacity scales, a growing fraction of computing resources remains idle under single-tenant allocations. Spatial partitioning addresses this need by offering superior throughput. However, it lacks container-level enforcement, causing resource over-consumption in multi-tenant environments. We present SpaceFlow, a runtime enforcement mechanism that achieves container-level GPU spatial partitioning by transparently intercepting GPU runtime interface and unifying its contexts. To ensure secure deployment, SpaceFlow integrates with a cloud-native ecosystem and eliminates resource oversubscription by immutable resource configuration. For broad compatibility, SpaceFlow includes adaptive fallback mechanisms that maintain container partitioning on GPUs with the latest architecture features. We validate SpaceFlow across various GPU types in a cloud-native cluster, demonstrating significant reduction in cross-container interference compared with conventional process-level spatial partitioning. SpaceFlow achieves up to 11.34 ×  interference reduction in evaluated scenarios while maintaining  ≤ 3.0% overhead.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Juwon2022AMBLE,

title = {AMBLE: Adjusting Mini-Batch and Local Epoch for Federated Learning with Heterogeneous Devices},

author = {Juwon Park and Daegun Yoon and Sangho Yeo and Sangyoon Oh},

url = {https://www.sciencedirect.com/science/article/pii/S0743731522001757},

doi = {https://doi.org/10.1016/j.jpdc.2022.07.009},

issn = {0743-7315},

year  = {2022},

date = {2022-07-21},

urldate = {2022-07-21},

journal = {Journal of Parallel and Distributed Computing},

abstract = {As data privacy becomes increasingly important, federated learning applied to the training of deep learning models while ensuring the data privacy of devices is entering the spotlight. Federated learning makes it possible to process all data at once while processing data independently from various devices without collecting distributed local data in a central server. However, there are still challenges to overcome for the system of devices in federated learning such as communication overheads and the heterogeneity of the system. In this paper, we propose the Adjusting Mini-Batch and Local Epoch (AMBLE) approach, which adaptively adjusts the local mini-batch and local epoch size for heterogeneous devices in federated learning and updates the parameters synchronously. With AMBLE, we enhance the computational efficiency by removing stragglers and scaling the local learning rate to improve the model convergence rate and accuracy. We verify that federated learning with AMBLE is a stably trained model with a faster convergence speed and higher accuracy than FedAvg and adaptive batch size scheme for both identically and independently distributed (IID) and non-IID cases.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{s21248416,

title = {Mitigating Cold Start Problem in Serverless Computing with Function Fusion},

author = {Seungjun Lee and Daegun Yoon and Sangho Yeo and Sangyoon Oh},

url = {https://www.mdpi.com/1424-8220/21/24/8416},

doi = {10.3390/s21248416},

issn = {1424-8220},

year  = {2021},

date = {2021-12-23},

urldate = {2021-12-16},

journal = {Sensors},

volume = {21},

number = {24},

abstract = {As Artificial Intelligence (AI) is becoming ubiquitous in many applications, serverless computing is also emerging as a building block for developing cloud-based AI services. Serverless computing has received much interest because of its simplicity, scalability, and resource efficiency. However, due to the trade-off with resource efficiency, serverless computing suffers from the cold start problem, that is, a latency between a request arrival and function execution. The cold start problem significantly influences the overall response time of workflow that consists of functions because the cold start may occur in every function within the workflow. Function fusion can be one of the solutions to mitigate the cold start latency of a workflow. If two functions are fused into a single function, the cold start of the second function is removed; however, if parallel functions are fused, the workflow response time can be increased because the parallel functions run sequentially even if the cold start latency is reduced. This study presents an approach to mitigate the cold start latency of a workflow using function fusion while considering a parallel run. First, we identify three latencies that affect response time, present a workflow response time model considering the latency, and efficiently find a fusion solution that can optimize the response time on the cold start. Our method shows a response time of 28%–86% of the response time of the original workflow in five workflows.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{yeo2021achieving,

title = {Achieving Balanced Load Distribution with Reinforcement Learning-Based Switch Migration in Distributed SDN Controllers},

author = {Sangho Yeo and Ye Naing and Taeha Kim and Sangyoon Oh},

url = {https://www.mdpi.com/2079-9292/10/2/162},

year  = {2021},

date = {2021-01-01},

journal = {Electronics},

volume = {10},

number = {2},

pages = {162},

publisher = {Multidisciplinary Digital Publishing Institute},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{bae2020novel,

title = {Novel data-placement scheme for improving the data locality of Hadoop in heterogeneous environments},

author = {Minho Bae and Sangho Yeo and Gyudong Park and Sangyoon Oh},

url = {https://doi.org/10.1002/cpe.5752},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Concurrency and Computation: Practice and Experience},

pages = {e5752},

publisher = {Wiley Online Library},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{yeo2020accelerated,

title = {Accelerated deep reinforcement learning with efficient demonstration utilization techniques},

author = {Sangho Yeo and Sangyoon Oh and Minsu Lee},

url = {https://link.springer.com/article/10.1007/s11280-019-00763-0},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {World Wide Web},

pages = {1--23},

publisher = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{seo2018active,

title = {Active-Active Message Replica Scheme for Enhancing Performance of Distributed Message Broker},

author = {Kyeonghee Seo and Sangho Yeo and Sangyoon Oh},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {KIPS Transactions on Computer and Communication Systems},

volume = {7},

number = {6},

pages = {145--154},

publisher = {Korea Information Processing Society},

keywords = {},

pubstate = {published},

tppubtype = {article}

}