이승준
Seungjun Lee
henry174 at ajou.ac.kr
After graduation
SK Planet
Publications
2023
6.
Lee, Seungjun; Yu, Miri; Yoon, Daegun; Oh, Sangyoon
Can hierarchical client clustering mitigate the data heterogeneity effect in federated learning? Conference
2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2023, ISBN: 979-8-3503-1200-3.
@conference{nokey,
title = {Can hierarchical client clustering mitigate the data heterogeneity effect in federated learning?},
author = {Seungjun Lee and Miri Yu and Daegun Yoon and Sangyoon Oh},
url = {10.1109/IPDPSW59300.2023.00134},
doi = {10.1109/IPDPSW59300.2023.00134},
isbn = {979-8-3503-1200-3},
year = {2023},
date = {2023-05-15},
urldate = {2023-05-15},
booktitle = {2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
abstract = {Federated learning (FL) was proposed for training a deep neural network model using millions of user data. The technique has attracted considerable attention owing to its privacy-preserving characteristic. However, two major challenges exist. The first is the limitation of simultaneously participating clients. If the number of clients increases, the single parameter server easily becomes a bottleneck and is prone to have stragglers. The second is data heterogeneity, which adversely affects the accuracy of the global model. Because data should remain at user devices to preserve privacy, we cannot use data shuffling, which is used to homogenize training data in traditional distributed deep learning. We propose a client clustering and model aggregation method, CCFed, to increase the number of simultaneously participating clients and mitigate the data heterogeneity problem. CCFed improves the learning performance using set partition modeling to let data be evenly distributed between clusters and mitigate the effect of a non-IID environment. Experiments show that we can achieve a 2.7-14% higher accuracy using CCFed compared with FedAvg, where CCFed requires approximately 50% less number of rounds compared with FedAvg training on benchmark datasets.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Federated learning (FL) was proposed for training a deep neural network model using millions of user data. The technique has attracted considerable attention owing to its privacy-preserving characteristic. However, two major challenges exist. The first is the limitation of simultaneously participating clients. If the number of clients increases, the single parameter server easily becomes a bottleneck and is prone to have stragglers. The second is data heterogeneity, which adversely affects the accuracy of the global model. Because data should remain at user devices to preserve privacy, we cannot use data shuffling, which is used to homogenize training data in traditional distributed deep learning. We propose a client clustering and model aggregation method, CCFed, to increase the number of simultaneously participating clients and mitigate the data heterogeneity problem. CCFed improves the learning performance using set partition modeling to let data be evenly distributed between clusters and mitigate the effect of a non-IID environment. Experiments show that we can achieve a 2.7-14% higher accuracy using CCFed compared with FedAvg, where CCFed requires approximately 50% less number of rounds compared with FedAvg training on benchmark datasets.
2022
5.
Lee, Seungjun; Jeong, Minjoong; Oh, Sangyoon
Is Ant Colony System better than FFD for VM placement in a heterogeneous cluster? Conference
2022 IEEE International Conference on Cloud Engineering (IC2E), 2022, ISBN: 978-1-6654-9116-7.
@conference{Seungjun2022Ant,
title = {Is Ant Colony System better than FFD for VM placement in a heterogeneous cluster?},
author = {Seungjun Lee and Minjoong Jeong and Sangyoon Oh},
url = {https://ieeexplore.ieee.org/document/9946320},
doi = {10.1109/IC2E55432.2022.00038},
isbn = {978-1-6654-9116-7},
year = {2022},
date = {2022-09-22},
urldate = {2022-06-11},
booktitle = {2022 IEEE International Conference on Cloud Engineering (IC2E)},
pages = {277-278},
abstract = {First fit decreasing (FFD) is the most popular heuristic for virtual machine (VM) placement problems. However, FFD does not perform well in a heterogeneous cluster environment in which physical machines have different capacities. Moreover, FFD and other heuristics, such as best fit decreasing (BFD), do not effectively handle the VM placement problem if multiple resources are considered together. In this study, we analyze the reason why the ant colony system performs better than FFD for VM placement in a heterogeneous cluster. We verified our logical observations through experimental comparisons with other heuristics.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
First fit decreasing (FFD) is the most popular heuristic for virtual machine (VM) placement problems. However, FFD does not perform well in a heterogeneous cluster environment in which physical machines have different capacities. Moreover, FFD and other heuristics, such as best fit decreasing (BFD), do not effectively handle the VM placement problem if multiple resources are considered together. In this study, we analyze the reason why the ant colony system performs better than FFD for VM placement in a heterogeneous cluster. We verified our logical observations through experimental comparisons with other heuristics.
2021
4.
Lee, Seungjun; Yoon, Daegun; Yeo, Sangho; Oh, Sangyoon
Mitigating Cold Start Problem in Serverless Computing with Function Fusion Journal Article
In: Sensors, vol. 21, no. 24, 2021, ISSN: 1424-8220.
@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}
}
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.
3.
Yu, Miri; Lee, Seungjun; Oh, Sangyoon
Energy-aware container migration scheme in edge computing for fault-tolerant fire-disaster response system Conference
The 7th International Conference on Next Generation Computing 2021, 2021.
@conference{Yu2021container,
title = {Energy-aware container migration scheme in edge computing for fault-tolerant fire-disaster response system},
author = {Miri Yu and Seungjun Lee and Sangyoon Oh},
year = {2021},
date = {2021-11-05},
urldate = {2021-11-05},
booktitle = {The 7th International Conference on Next Generation Computing 2021},
abstract = {In light of the recent advancements made in IT, many researchers are studying and exploring ways to minimize damage from fire disasters using artificial intelligence and cloud technology. With the introduction of edge computing, fire-disaster response software systems have made significant progress. However, existing studies often do not consider the response to a sudden power supply cut-off due to fire. In this study, we propose a container migration scheme based on the first-fit-decreasing algorithm of bin-packing problem and 0-1 knapsack algorithm to provide fault tolerance for containers running on edge servers that are powered off.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
In light of the recent advancements made in IT, many researchers are studying and exploring ways to minimize damage from fire disasters using artificial intelligence and cloud technology. With the introduction of edge computing, fire-disaster response software systems have made significant progress. However, existing studies often do not consider the response to a sudden power supply cut-off due to fire. In this study, we propose a container migration scheme based on the first-fit-decreasing algorithm of bin-packing problem and 0-1 knapsack algorithm to provide fault tolerance for containers running on edge servers that are powered off.
2.
Lee, Seungjun; Yoon, Daegun; Oh, Sangyoon
Imitation learning for VM placement problem using demonstration data generated by heuristics Conference
17th Int. Conference on Data Science (ICDATA’21), 2021.
@conference{lee2021imitation,
title = {Imitation learning for VM placement problem using demonstration data generated by heuristics},
author = {Seungjun Lee and Daegun Yoon and Sangyoon Oh},
url = {https://youtu.be/CmG3E1rWroQ},
year = {2021},
date = {2021-07-26},
urldate = {2021-07-26},
booktitle = {17th Int. Conference on Data Science (ICDATA’21)},
abstract = {Data centers are key components of cloud computing to run virtual machines. For saving the cost to operate data centers, it is important to decide how to allocate each virtual machine to a certain physical machine. Because the virtual machine placement problem is NP-Hard, there are many heuristics to obtain near-optimal solutions as quickly as possible. The reinforcement learning technique can be applied for virtual machine placement problem. However, if the problem size gets bigger, the convergence speed of reinforcement learning gets slower. The possible solution is that the agent imitates the behavior of given demonstration, called imitation learning. In this paper, we propose a method combining reinforcement learning with imitation learning. In our proposed approach, demonstration data is generated by simple heuristics not human experts.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Data centers are key components of cloud computing to run virtual machines. For saving the cost to operate data centers, it is important to decide how to allocate each virtual machine to a certain physical machine. Because the virtual machine placement problem is NP-Hard, there are many heuristics to obtain near-optimal solutions as quickly as possible. The reinforcement learning technique can be applied for virtual machine placement problem. However, if the problem size gets bigger, the convergence speed of reinforcement learning gets slower. The possible solution is that the agent imitates the behavior of given demonstration, called imitation learning. In this paper, we propose a method combining reinforcement learning with imitation learning. In our proposed approach, demonstration data is generated by simple heuristics not human experts.
2020
1.
Yeo, Sangho; Lee, Seungjun; Choi, Boreum; Oh, Sangyoon
2020 International Conference on Information and Communication Technology Convergence (ICTC), IEEE 2020.
@conference{yeo2020integrate,
title = {Integrate multi-agent simulation environment and multi-agent reinforcement learning (MARL) for real-world scenario},
author = {Sangho Yeo and Seungjun Lee and Boreum Choi and Sangyoon Oh},
url = {https://ieeexplore.ieee.org/abstract/document/9289369},
doi = {10.1109/ICTC49870.2020.9289369},
year = {2020},
date = {2020-01-01},
booktitle = {2020 International Conference on Information and Communication Technology Convergence (ICTC)},
pages = {523--525},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}