請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/27186
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 劉邦鋒(Pangfeng Liu) | |
dc.contributor.author | Sih-Wei Chen | en |
dc.contributor.author | 陳思瑋 | zh_TW |
dc.date.accessioned | 2021-06-12T17:57:26Z | - |
dc.date.available | 2016-08-16 | |
dc.date.copyright | 2011-08-16 | |
dc.date.issued | 2011 | |
dc.date.submitted | 2011-08-09 | |
dc.identifier.citation | [1] F.D. Sacerdoti, M.J. Katz, M.L. Massie, and D.E. Culler. Wide area cluster monitoring with
ganglia. In Cluster Computing, 2003. Proceedings. 2003 IEEE International Conference on, pages 289–298, December 2003. [2] M.L. Massie, B.N. Chun, and D.E. Culler. The ganglia distributed monitoring system: de- sign, implementation, and experience. Parallel Computing, 30(7):817–840, 2004. [3] Glusterfs. http://www.gluster.org/. [4] Amazon elastic compute cloud. http://aws.amazon.com/ec2/. [5] Google. http://www.google.com/. [6] D. Nurmi, R. Wolski, C. Grzegorczyk, G. Obertelli, S. Soman, L. Youseff, and D. Zagorod- nov. The eucalyptus open-source cloud-computing system. In Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, CddCGRID ’09, pages 124–131, Washington, DC, USA, 2009. IEEE Computer Society. [7] Opennebula. http://opennebula.org/. [8] P. Barham, B. Dragovic, K. Fraser, S. Hand, T. Harris, A. Ho, R. Neugebauer, I. Pratt, and A. Warfield. Xen and the art of virtualization. In Proceedings of the nineteenth ACM symposium on Operating systems principles, SOSP ’03, pages 164–177, New York, NY, USA, 2003. ACM. [9] Vmware. http://www.vmware.com/. [10] L.M. Vaquero, L. Rodero-Merino, and R. Buyya. Dynamically scaling applications in the cloud. ACM SIGCOMM Computer Communication Review, 41:45–52, January 2011. [11] Scalr. http://www.scalr.net/. [12] Rightscale. http://www.rightscale.com/. [13] T.C. Chieu, A. Mohindra, A.A. Karve, and A. Segal. Dynamic scaling of web applications in a virtualized cloud computing environment. In e-Business Engineering, 2009. ICEBE ’09. IEEE International Conference on, pages 281–286, October 2009. [14] M. Mao, J. Li, and M. Humphrey. Cloud auto-scaling with deadline and budget constraints. In Grid Computing (GRID), 2010 11th IEEE/ACM International Conference on, pages 41– 48, October 2010. [15] E. Caron, F. Desprez, and A.Muresan. Forecasting for grid and cloud computing on-demand resources based on pattern matching. In Cloud Computing Technology and Science (Cloud- Com), 2010 IEEE Second International Conference on, pages 456–463, December 2010. [16] D. Gmach, J. Rolia, L. Cherkasova, and A. Kemper. Workload analysis and demand pre- diction of enterprise data center applications. In Workload Characterization, 2007. IISWC 2007. IEEE 10th International Symposium on, pages 171–180, September 2007. [17] Nagios. http://www.nagios.org/. [18] F. Raimondi, J. Skene, and W. Emmerich. Efficient online monitoring of web-service slas. In Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of soft- ware engineering, SIGSOFT ’08/FSE-16, pages 170–180, New York, NY, USA, 2008. ACM. [19] Q. Wang, Y. Liu, M. Li, and H. Mei. An online monitoring approach for web services. In Computer Software and Applications Conference, 2007. COMPSAC 2007. 31st Annual International, pages 335–342, July 2007. [20] N. Goel and R.K. Shyamasundar. Automatic monitoring of slas of web services. In Services Computing Conference (APSCC), 2010 IEEE Asia-Pacific, pages 99–106, December 2010. [21] F. Karim. A peer-to-peer approach to providing qos monitoring for web service activities. In Proceedings of the ACM/IFIP/USENIXMiddleware ’08 Conference Companion, Companion ’08, pages 7–11, New York, NY, USA, 2008. ACM. [22] N. Artaiam and T. Senivongse. Enhancing service-side qos monitoring for web services. In Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Com- puting, 2008. SNPD ’08. Ninth ACIS International Conference on, pages 765–770, August 2008. [23] S.A. Weil, S.A. Brandt, E.L. Miller, D.D.E Long, and C. Maltzahn. Ceph: a scalable, high- performance distributed file system. In Proceedings of the 7th symposium on Operating systems design and implementation, OSDI ’06, pages 307–320, Berkeley, CA, USA, 2006. USENIX Association. [24] J.C. Wu and S.A.Brandt. Providing quality of service support in object-based file system. In Mass Storage Systems and Technologies, 2007. MSST 2007. 24th IEEE Conference on, pages 157–170, September 2007. [25] M. Madruga, S. Loest, and C. Maziero. Using transparent files in a fault tolerant distributed file system. In Autonomous Decentralized Systems, 2009. ISADS ’09. International Sympo- sium on, pages 1–6, March 2009. [26] Nginx. http://nginx.net/. [27] Akamai and jupiterresearch identify ‘4 Seconds’ as the new threshold of acceptability for retail web page response times. http://www.akamai.com/html/about/press/releases/2006/press 110606.html. [28] Mediawiki. http://www.mediawiki.org/. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/27186 | - |
dc.description.abstract | 許多企業使用網頁應用程式來提拱服務。 這些網頁應用程式的工作量是會大量的變化的。 為了能夠服務最大工作量而在平日也使用如此多的資源將會造成資源的浪費。 雲端運算以及自動服務水準調整能夠提供網頁應用程式根據不同工作量調整所需資源的能力。 此篇論文我們將實作水平式服務水準調整系統。 並說明此系統細節以及所使用之前端負載平衡統-後端網頁伺服器的架構。 另外為了節省開啟虛擬機器的時間,我們使用了虛擬機器池的概念。 我們針對網頁應用程式提出了兩不同的調整演算法。 連線數演算法會根據目前連線數來決定是否需要對機器數目做調整。 趨勢預測演算法將會工作量的趨勢做預測,並且減少不必要的加、減機器決定。 我們的實驗結果顯示出我們的系統對於凸如其來的工作量能夠如期低做出反應,調整虛擬機器數目。 定且對於有周期性工作量,能夠避免不必要的調整。 | zh_TW |
dc.description.abstract | Many enterprises use web application to provide their service. Web application workloads fluctuated in a large range. Maintaining the resource to meet the peak workload is costly. Cloud computing and auto scaling provide web application on-demand resources to deal with fluctuating workload. In this paper, we implement an horizontal auto scaling module. We will illustrate our auto scaling module with front-end load balancer and back-end web servers. We use virtual machine pool to save the time of booting virtual machines. We also propose two scaling algorithms. Connection based algorithm scales based on connection numbers. Trend prediction predicts the trend of workload changing. It adjusts scaling decision to avoid unnecessary scaling. Our work has demonstrated our auto scaling module can handle peak workload and avoid unnecessary scaling decisions in period workload. | en |
dc.description.provenance | Made available in DSpace on 2021-06-12T17:57:26Z (GMT). No. of bitstreams: 1 ntu-100-R98922087-1.pdf: 567823 bytes, checksum: df22e834a7c02d8ba9adc5ceb188231a (MD5) Previous issue date: 2011 | en |
dc.description.tableofcontents | Certification i
Acknowledgement ii Chinese Abstract iii Abstract iv 1 Introduction 1 2 Related Work 4 3 Architecture 6 3.1 System Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.1.1 Auto Scaling Master . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.1.2 VM Pool Manager . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.1.3 Distributed File System . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.2 System Work Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 4 Implementation 9 4.1 Auto Scaling Master . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 4.1.1 Monitor: Ganglia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 4.1.2 Load Balancer: Nginx . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 4.1.3 Decision Maker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 4.2 VM Pool Manager . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.2.1 Pool Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 4.2.2 Request Server . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 4.2.3 Master Register . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 4.3 Distributed File System: Gluster . . . . . . . . . . . . . . . . . . . . . . . . . . 14 4.4 VM Prototype . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 5 Algorithm 16 5.1 Majority Vote . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 5.1.1 Example:scale out . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 5.1.2 Example:scale in . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 5.2 Connection Based Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 5.3 Trend Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 5.3.1 Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 5.4 Algorithm Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 6 Experimentation 25 6.1 Environment Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 6.2 Comparison of Load Balancer Algorithm . . . . . . . . . . . . . . . . . . . . . 26 6.2.1 Experiment Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 6.2.2 Experiment Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 6.3 Comparison of Scaling Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . 28 6.3.1 Experiment Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 6.3.2 Experiment Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 6.4 Peak Workload . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 6.4.1 Experiment Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 6.4.2 Experiment Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 6.5 Periodic Workload . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 6.5.1 Experiment Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 6.5.2 Experiment Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 7 Conclusion 36 Bibliography 37 | |
dc.language.iso | en | |
dc.title | 雲端計算自動服務水準調整系統 | zh_TW |
dc.title | Auto Resource Scaling for Cloud Computing | en |
dc.type | Thesis | |
dc.date.schoolyear | 99-2 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 吳真貞(Jan-Jan Wu) | |
dc.contributor.oralexamcommittee | 洪士灝(Shih-Hao Hung) | |
dc.subject.keyword | 雲端運算,自動服務水準調整, | zh_TW |
dc.subject.keyword | Cloud computing,Auto scaling, | en |
dc.relation.page | 39 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2011-08-09 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-100-1.pdf 目前未授權公開取用 | 554.51 kB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。