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Conference Paper
DeepVM: Integrating Spot and On-Demand VMs for Cost-Efficient Deep Learning Clusters in the Cloud
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- Authors
- Yoochan Kim, Kihyun Kim, Yonghyeon Cho, Jinwoo Kim, Awais Khan, Ki-Dong Kang, Baik-Song An, Myung-Hoon Cha, Hong-Yeon Kim, Youngjae Kim
- Issue Date
- 2024-05
- Citation
- International Symposium on Cluster, Cloud and Internet Computing (CCGrid) 2024, pp.227-235
- Language
- English
- Type
- Conference Paper
- Abstract
- Distributed Deep Learning (DDL), as a paradigm, dictates the use of GPU-based clusters as the optimal infrastructure for training large-scale Deep Neural Networks (DNNs). However, the high cost of such resources makes them inaccessible to many users. Public cloud services, particularly Spot Virtual Machines (VMs), offer a cost-effective alternative, but their unpredictable availability poses a significant challenge to the crucial checkpointing process in DDL. To address this, we introduce DeepVM, a novel solution that recommends cost-effective cluster configurations by intelligently balancing the use of Spot and On-Demand VMs. DeepVM leverages a four-stage process that analyzes instance performance using the FLOPP (FLoating-point Operations Per Price) metric, performs architecture-level analysis with linear programming, and identifies the optimal configuration for the user-specific needs. Extensive simulations and real-world deployments in the AWS environment demonstrate that DeepVM consistently outperforms other policies, reducing training costs and overall makespan. By enabling cost-effective checkpointing with Spot VMs, DeepVM opens up DDL to a wider range of users and facilitates a more efficient training of complex DNNs.
- KSP Keywords
- Cloud service, Cost-efficient, Deep neural network(DNN), Floating-point operations, GPU-based, On-demand, Optimal Configuration, Public cloud, Real-world, User-specific, Virtual Machine(VM)