Earticle

초록

영어

We propose a container orchestration system for process workloads that combines the potential of big data and machine learning technologies to integrate enterprise process-centric workloads. This proposed system analyzes big data generated from industrial automation to identify hidden patterns and build a machine learning prediction model. For each machine learning case, training data is loaded into a data store and preprocessed for model training. In the next step, you can use the training data to select and apply an appropriate model. Then evaluate the model using the following test data: This step is called model construction and can be performed in a deployment framework. Additionally, a visual hierarchy is constructed to display prediction results and facilitate big data analysis. In order to implement parallel computing of PCA in the proposed system, several virtual systems were implemented to build the cluster required for the big data cluster. The implementation for evaluation and analysis built the necessary clusters by creating multiple virtual machines in a big data cluster to implement parallel computation of PCA. The proposed system is modeled as layers of individual components that can be connected together. The advantage of a system is that components can be added, replaced, or reused without affecting the rest of the system.

목차

Abstract
1. INTRODUCTION
2. RELATED WORK
3. PROPOSED SYSTEM: CONTAINER ORCHESTRATION SYSTEM FOR PROCESS WORKLOADS TITLE AND AUTHOR INFORMATION
3.1 System Component
3.2 Sequence Diagram
3.3 Principal Component Analysis
4. APPLICATION CASES AND COMPERATIVE ANALYSIS
5. CONCLUSION
ACKNOWLEDGEMENT
REFERENCES

키워드

저자

• Jong-Sub Lee [ Professor, College of General Education, SeMyung University, Jecheon, Korea ]
• Seok-Jae Moon [ Professor, Department of Artificial Intelligence Institute of Information Technology, KwangWoon University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380