Earticle

초록

영어

Cloud computing has become a critical infrastructure for large-scale data processing and machine learning applications. However, task failures frequently occur due to distributed resources and dynamic operating environments, leading to service delays and resource waste. To address this issue, this paper proposes a task failure prediction system based on stacking ensemble learning. The proposed system is structured in three layers: log collection, prediction, and result application. Base model predictions are integrated by a metamodel to produce the final outcome. Experiments conducted with the Google Cluster Trace 2019 dataset demonstrate that the proposed system outperforms single models in terms of prediction accuracy and stability, providing a robust and scalable framework suitable for real-world cloud environments.

목차

Abstract
1. Introduction
2. Related Work
3. Proposed System
3.1 System Overview
3.2 Sequence Diagram
3.3 Component Algorithms
4. Experimental Results and Comparative Analysis
4.1 Dataset and Preprocessing
4.2 Performance Evaluation
4.3 Comparative Analysis
5. Conclusion
Acknowledgement
References

키워드

저자

• Ka-Bin Lee [ The master’s course, Graduate School of Smart Convergence, Kwangwoon University, Seoul, Korea ]
• Seok-Jae Moon [ Professor, Graduate School of Smart Convergence, Kwangwoon University, Seoul, 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