Jeonghyeon Park

News
- Activities
- Award
Papers
- Domestic
- International
  - IEEE Cloud
Profile
Project
Home

Contact

Copyright © 2024 | Yankos Modifications by Jhpark

Home > Papers > International

Now Loading ...

International

Carbon-Aware and Fault-Tolerant Migration of Deep Learning Workloads in the Geo-distributed Cloud
The Overview of the proposed method(CAFTM) Paper information Carbon-Aware and Fault-Tolerant Migration of Deep Learning Workloads in the Geo-distributed Cloud Jeonghyeon Park, Daero Kim, Jiseon Kim, Jungkyu Han, Sejin Chun (2024). IEEE International Conference on Cloud Computing (CLOUD) Keywords carbon-aware, fault-tolerant, geo-distributed, cloud, deep learning, task migration Abstract Recently, many deep learning models have been trained in geographically distributed data centers. The carbon emissions produced by training the models may pose a significant threat to climate change like increasing temperatures. Existing studies have a hardship in shifting the workload of training models to a data center with low carbon emissions. So, they fail to ensure low emissions of the workload during training, especially when long-term workloads like Large Language Models (LLMs) are trained. To cope with this problem, we propose a method that shifts the workload to a cloud with low carbon emissions while enduring a lack of computational resources. Specifically, we define a task scheduler that includes states and their transitions to migrate mini-batches dynamically. Next, we present a fault-tolerant control that optimizes a GPU frequency to adapt to workload variations of training models while guaranteeing its power consumption. Last, we conducted exhaustive experiments using real-world data in terms of carbon emissions, transfer time, and power consumption compared to state-of-the-art methods. Cite @INPROCEEDINGS{10643899, author={Park, Jeonghyeon and Kim, Daero and Kim, Jiseon and Han, Jungkyu and Chun, Sejin}, booktitle={2024 IEEE 17th International Conference on Cloud Computing (CLOUD)}, title={Carbon-Aware and Fault-Tolerant Migration of Deep Learning Workloads in the Geo-Distributed Cloud}, year={2024}, pages={494-501}, }
Papers · 2024-06-03

Touch background to close