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研究生:林冠勳
研究生(外文):Kuan-Hsun Lin
論文名稱:基於DevOps自動化雲端沙箱環境實踐自動擴展技術及GPU虛擬化
論文名稱(外文):Autoscaling and GPU Virtualization Implementation based on Automated Cloud Sandbox Environment for DevOps
指導教授:王凡
指導教授(外文):Farn Wang
口試委員:陳銘憲張純明戴顯權
口試委員(外文):Ming-Syan Chen
口試日期:2018-07-18
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:電子工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2018
畢業學年度:106
語文別:英文
論文頁數:31
中文關鍵詞:敏捷軟體開發雲端軟體工程軟體工程工具及環境DevOpsOpenstack
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  • 被引用被引用:0
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近年來,雲端運算在各種領域上的應用越來越多,對於雲端環境的架設也發展了許多解決方案。Autoscaling就是一種使用在雲端運算中,用來監控和管理資源的一種方法。本篇論文主要介紹利用開放原始碼的雲端運算系統OpenStack來實作一個快速供裝工具AutoScaling Provisioning (ASP)。ASP主要用途為讓使用者採取DevOps方法來做軟體開發流程時,能夠快速且自動化的部建整個雲端環境,且架設的雲端伺服器能夠具有Autoscaling及虛擬化GPU的功能。ASP會接受用戶所撰寫的設定文件,包括參數和配置的腳本,然後自動創建AutoScalingGroup(ASG),最後將它們轉換為Heat Orchestration Template(HOT)的形式以進行雲端沙箱部署。在本篇論文的實驗裡,我們的結果展示出,ASP確實可以加速部署DevOps在雲端環境中使用Autoscaling和GPU的流程。
Autoscaling is a method used in cloud computing for monitoring resources in the cloud environment and scales automatically based on the load of a server. In this paper, we introduce an automatic provisioning tool AutoScaling Provisioning (ASP) to implement auto-scaling with GPU virtualization in an automated OpenStack cloud sandbox environment for DevOps flow. ASP accepts user’s document including parameters and configured scripts, and then automatically creates an AutoScalingGroup (ASG), at last coverts them to a Heat Orchestration Template (HOT) for cloud sandbox deployment. Our evaluation shows that ASP can speed up against about using autoscaling and GPU in cloud sandbox for DevOps.
口試委員會審定書 i
誌謝 ii
中文摘要 iii
ABSTRACT iv
CONTENTS v
LIST OF FIGURES vii
LIST OF TABLES viii
Chapter 1 INTRODUCTION 1
Chapter 2 BACKGROUND 4
2.1 OpenStack 4
2.2 Heat 5
2.3 Ceilometer 7
2.4 Autoscaling 9
2.5 GPU Virtualization 9
2.6 DevOps Deployment 10
2.7 Continuous Integration 10
Chapter 3 AUTOSCALING PROVISIONING (ASP) 11
3.1 DevOps workflow in ASP 11
3.2 Build Stage 13
3.3 Deploy Stage 15
Chapter 4 IMPLEMENTATION 18
4.1 PCI passthrough 18
4.2 ASP Parser 20
4.2.1 Build Stage & Deploy Stage Parser 21
Chapter 5 EXPERIMENT 23
Chapter 6 CONCLUSION 29

REFERENCE 30
[1] B. Hayes. “Cloud Computing”, Communications of ACM, Vol. 51, No. 7, July 2008.
[2] C. Ebert, G. Gallardo, J. Hernantes, N. Serrano. “DevOps”, IEEE Software, May/June 2016.
[3] O. Sefraoui, M. Aissaoui, M. Eleuldj. “OpenStack: Toward an Open-source Solution for Cloud Computing”, International Journal of Computer Applications, Volume 55 - No. 03, October 2012.
[4] L. Beernaert, M. Matos, R. Vilaça, R. Oliveira. “Automatic Elasticity in OpenStack”, SDMCMM, December 2012.
[5] H. Kang, M. Le, S. Tao. “Container and microservice driven design for cloud infrastructure devops”, Cloud Engineering (IC2E), IEEE, April 2016.
[6] Y. Yamato, M. Muroi, K. Tanaka, M. Uchimura. “Development of template management technology for easy deployment of virtual resources on OpenStack”, Journal of Cloud Computing, December 2014.
[7] Y. Yamato. “Automatic system test technology of virtual machine software patch on IaaS cloud”, IEEJ, Volume10, IssueS1, October 2015.
[8] M. Mao, M. Humphrey. "Auto-scaling to minimize cost and meet application deadlines in cloud workflows", SC11, November 2011.
[9] C.-T. Yang, H.-Y. Wang, W.-S. Ou. “On implementation of GPU virtualization using PCI pass-through”, CloudCom12, Dec. 2012.
[10] M. Meyer. "Continuous integration and its tools", IEEE software, April 2014.
[11] R. Punjabi, R.Bajaj. “User stories to user reality: A DevOps approach for the cloud”, RTEICT, IEEE, May 2016.
[12] H.L. Akshaya, S. N. Jagadish, J. Vidya, K. Veena. “A Basic Introduction to DevOps Tools”, IJCSIT, Vol.6 (3), 2015.
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