Publications
Detailed Information
Predicting live migration performance of virtual machines using machine learning : 기계 학습을 통한 가상화 플랫폼의 라이브 마이그레이션 성능 예측
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Bernhard Egger | - |
dc.contributor.author | 송진호 | - |
dc.date.accessioned | 2017-07-14T03:02:53Z | - |
dc.date.available | 2017-07-14T03:02:53Z | - |
dc.date.issued | 2016-02 | - |
dc.identifier.other | 000000132633 | - |
dc.identifier.uri | https://hdl.handle.net/10371/123209 | - |
dc.description | 학위논문 (석사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 2. Bernhard Egger. | - |
dc.description.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. | - |
dc.description.tableofcontents | Chapter 1 Introduction 1
Chapter 2 Background and related work 4 2.1 Live migration algorithms 5 2.2 Performance metrics 9 2.3 Existing models and evaluation attempts 11 Chapter 3 Empirical Evaluation 13 3.1 Sample generation and evaluation 13 3.2 Workloads 14 Chapter 4 Data driven approach 23 4.1 Parameter selection and migration algorithms 23 4.2 Prediction using support vector regression 24 4.3 Tool architecture 26 4.4 Single vs. multiple predictors 27 Chapter 5 Experimental evaluation 29 5.1 Training setup 29 5.2 Prediction results 30 Chapter 6 Conclusion 37 Bibliography 38 Abstract in Korean 41 | - |
dc.format | application/pdf | - |
dc.format.extent | 1073783 bytes | - |
dc.format.medium | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | 서울대학교 대학원 | - |
dc.subject | virtualization | - |
dc.subject | live migration | - |
dc.subject | machine learning | - |
dc.subject | support vector machine | - |
dc.subject.ddc | 621 | - |
dc.title | Predicting live migration performance of virtual machines using machine learning | - |
dc.title.alternative | 기계 학습을 통한 가상화 플랫폼의 라이브 마이그레이션 성능 예측 | - |
dc.type | Thesis | - |
dc.contributor.AlternativeAuthor | Jinho Song | - |
dc.description.degree | Master | - |
dc.citation.pages | 42 | - |
dc.contributor.affiliation | 공과대학 전기·컴퓨터공학부 | - |
dc.date.awarded | 2016-02 | - |
- Appears in Collections:
- Files in This Item:
Item View & Download Count
Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.