Earticle

초록

영어

Recently, IoT systems are cloud-based, so that continuous and large amounts of data collected from sensor nodes are processed in the data server through the cloud. However, in the centralized configuration of large-scale cloud computing, computational processing must be performed at a physical location where data collection and processing take place, and the need for edge computers to reduce the network load of the cloud system is gradually expanding. In this paper, a cluster system consisting of 6 inexpensive Raspberry Pi boards was constructed to perform fast data processing. And we propose "Kubernetes cluster system(KCS)" for processing large data collection and analysis by model distribution and data pipeline method. To compare the performance of this study, an ensemble model of deep learning was built, and the accuracy, processing performance, and processing time through the proposed KCS system and model distribution were compared and analyzed. As a result, the ensemble model was excellent in accuracy, but the KCS implemented as a data pipeline proved to be superior in processing speed..

목차

Abstract
1. Introduction
2. Related Studies
2.1 Docker
2.2 Kubernetes
2.3 CNN
2.4 MobilenetV2
2.5 Distributed Deep Learning
3. System Design
3.1 Edge cluster for KCS
3.2 MobilenetV2 distributed pipeline model based on transfer learning
4. System Implementation and Performance Evaluation
4.1 Implementing a Kubernetes Edge Cluster
4.2 MobilenetV2 learning based on transfer learning
4.3 Implementation of distributed deep learning pipeline by model partitioning
4.4 Comparative evaluation for model performance
5. Conclusion
Acknowldegment
References

키워드

저자

• Sung-Ho Jeon [ Undergraduate Students, Dept. of Applied IT and Engineering, Pusan National University, Pusan ]
• Cheol-Gyu Lee [ Undergraduate Students, Dept. of Applied IT and Engineering, Pusan National University, Pusan ]
• Jae-Deok Lee [ Undergraduate Students, Dept. of Applied IT and Engineering, Pusan National University, Pusan ]
• Bo-Seok Kim [ Undergraduate Students, Dept. of Applied IT and Engineering, Pusan National University, Pusan ]
• Joo-Man Kim [ Professor, Dept. of Applied IT and Engineering, Pusan National University, Pusan ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380