Design and Implementation of Cloud Docker Application Architecture Based on Machine Learning in Container Management for Smart Manufacturing
Manufacturers are expanding their business-process innovation and customized manufacturing to reduce their information technology costs and increase their operational efficiency. Large companies are building enterprise-wide hybrid cloud platforms to further accelerate their digital transformation. M...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-07-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/13/6737 |
| _version_ | 1797480818666373120 |
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| author | Byoung Soo Kim Sang Hyeop Lee Ye Rim Lee Yong Hyun Park Jongpil Jeong |
| author_facet | Byoung Soo Kim Sang Hyeop Lee Ye Rim Lee Yong Hyun Park Jongpil Jeong |
| author_sort | Byoung Soo Kim |
| collection | DOAJ |
| description | Manufacturers are expanding their business-process innovation and customized manufacturing to reduce their information technology costs and increase their operational efficiency. Large companies are building enterprise-wide hybrid cloud platforms to further accelerate their digital transformation. Many companies are also introducing container virtualization technology to maximize their cloud transition and cloud benefits. However, small- and mid-sized manufacturers are struggling with their digital transformation owing to technological barriers. Herein, for small- and medium-sized manufacturing enterprises transitioning onto the cloud, we introduce a Docker Container application architecture, a customized container-based defect inspection machine-learning model for the AWS cloud environment developed for use in small manufacturing plants. By linking with open-source software, the development was improved and a datadog-based container monitoring system, built to enable real-time anomaly detection, was implemented. |
| first_indexed | 2024-03-09T22:05:38Z |
| format | Article |
| id | doaj.art-75476a69c0934b73aaacb68c1ff5089f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:05:38Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-75476a69c0934b73aaacb68c1ff5089f2023-11-23T19:41:52ZengMDPI AGApplied Sciences2076-34172022-07-011213673710.3390/app12136737Design and Implementation of Cloud Docker Application Architecture Based on Machine Learning in Container Management for Smart ManufacturingByoung Soo Kim0Sang Hyeop Lee1Ye Rim Lee2Yong Hyun Park3Jongpil Jeong4Department of Smart Factory Convergence, Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon 16419, KoreaDepartment of System Management Engineering, Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon 16419, KoreaDepartment of Mechanical Engineering, Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon 16419, KoreaDepartment of Mechanical Engineering, Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon 16419, KoreaDepartment of Smart Factory Convergence, Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon 16419, KoreaManufacturers are expanding their business-process innovation and customized manufacturing to reduce their information technology costs and increase their operational efficiency. Large companies are building enterprise-wide hybrid cloud platforms to further accelerate their digital transformation. Many companies are also introducing container virtualization technology to maximize their cloud transition and cloud benefits. However, small- and mid-sized manufacturers are struggling with their digital transformation owing to technological barriers. Herein, for small- and medium-sized manufacturing enterprises transitioning onto the cloud, we introduce a Docker Container application architecture, a customized container-based defect inspection machine-learning model for the AWS cloud environment developed for use in small manufacturing plants. By linking with open-source software, the development was improved and a datadog-based container monitoring system, built to enable real-time anomaly detection, was implemented.https://www.mdpi.com/2076-3417/12/13/6737cloud dockerdocker containermachine learningmonitoringsmart manufacturingcontainer management |
| spellingShingle | Byoung Soo Kim Sang Hyeop Lee Ye Rim Lee Yong Hyun Park Jongpil Jeong Design and Implementation of Cloud Docker Application Architecture Based on Machine Learning in Container Management for Smart Manufacturing Applied Sciences cloud docker docker container machine learning monitoring smart manufacturing container management |
| title | Design and Implementation of Cloud Docker Application Architecture Based on Machine Learning in Container Management for Smart Manufacturing |
| title_full | Design and Implementation of Cloud Docker Application Architecture Based on Machine Learning in Container Management for Smart Manufacturing |
| title_fullStr | Design and Implementation of Cloud Docker Application Architecture Based on Machine Learning in Container Management for Smart Manufacturing |
| title_full_unstemmed | Design and Implementation of Cloud Docker Application Architecture Based on Machine Learning in Container Management for Smart Manufacturing |
| title_short | Design and Implementation of Cloud Docker Application Architecture Based on Machine Learning in Container Management for Smart Manufacturing |
| title_sort | design and implementation of cloud docker application architecture based on machine learning in container management for smart manufacturing |
| topic | cloud docker docker container machine learning monitoring smart manufacturing container management |
| url | https://www.mdpi.com/2076-3417/12/13/6737 |
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