BROWSE
Detail
Conference Paper
Kubernetes-based DL Offloading Framework for Optimizing GPU Utilization in Edge Computing
Cited 2 time in 
Share 
Share
- Authors
- Chorwon Kim, Ryangsoo Kim, Geon-Yong Kim, Sungchang Kim
- Issue Date
- 2021-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2021, pp.143-146
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Recent advances in artificial intelligence, especially deep learning techniques, are attracting attention as promising solutions due to their high accuracy in a variety of applications. The deep learning technique requires high computational resources in the training and inference phases, and therefore, it is important to efficiently operate a deep learning application in edge servers equipped with limited computing resources. For efficiently operating deep learning applications in edge computing, the offloading method becomes one of the most promising solutions. In this paper, we introduce a deep learning offloading framework for optimizing GPU resource utilization in edge computing architecture. The proposed framework operates on an open-source container orchestration system, Kubernetes, which is used for managing multiple number of object detection offloading microservices running on an edge server. Through the real-world experimental results, we verify that the proposed framework enhances the performance of object detection offloading service by leveraging GPU utilization of edge server throughout applying Kubernetes-based container service orchestration.
- KSP Keywords
- Computing resources, Container orchestration, Deep learning application, Edge Computing, GPU utilization, High accuracy, Orchestration system, Real-world, Resource utilization, Service orchestration, artificial intelligence