원문정보
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
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