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Predicting live migration performance of virtual machines using machine learning : 기계 학습을 통한 가상화 플랫폼의 라이브 마이그레이션 성능 예측
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- Authors
- Advisor
- Bernhard Egger
- Major
- 공과대학 전기·컴퓨터공학부
- Issue Date
- 2016-02
- Publisher
- 서울대학교 대학원
- Keywords
- virtualization ; live migration ; machine learning ; support vector machine
- Description
- 학위논문 (석사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 2. Bernhard Egger.
- Abstract
- Virtualization is a widely used technology these days as most of server computing environments are rapidly shifting to cloud computing. Live migration, one of the most compelling features in system virtualization, has been an active area of research. Attempts to predict migration performance were made, but most of those were limited to analytical approaches with relatively unstable prediction errors or not easy to extend to realistic environments as more parameters are identified and considered. In this thesis, a novel data driven approach based on the support vector regression method providing flexibility and extensibility in parameter selection is introduced to predict performance metrics such as total migration time, downtime and the total amount of transferred data, especially on QEMU which is hardware virtualization platform that is open-source and the method of this thesis is easy to adapt to various purposes. It will facilitate automated system administration with live migration more efficiently.
- Language
- English
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