跳到主要內容

臺灣博碩士論文加值系統

(3.236.68.118) 您好!臺灣時間:2021/07/31 19:34
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:陳映先
研究生(外文):Ying-HsienChen
論文名稱:基於服務統計模型有效分配雲端虛擬機器資源之研究
論文名稱(外文):Resource Allocation on Cloud Virtual Machines Based on Service Statistics
指導教授:李忠憲李忠憲引用關係
指導教授(外文):Jung-Shian Li
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電腦與通信工程研究所
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:2012
畢業學年度:100
語文別:英文
論文頁數:43
中文關鍵詞:雲端運算資源分配資源模型
外文關鍵詞:Cloud ComputingResource AllocationResource Model
相關次數:
  • 被引用被引用:0
  • 點閱點閱:131
  • 評分評分:
  • 下載下載:9
  • 收藏至我的研究室書目清單書目收藏:0
雲端運算在這幾年迅速的發展,同時也是熱門的話題。透過虛擬化技術,我們可以優化現有資源的使用率,簡化基礎設施和軟體的管理。在我們的研究中,主要著重在基礎設施雲 (IaaS) 的資源分配,如何將資源有效的從主機分配給虛擬機器,提高整個系統的使用率。在虛擬機器被開啟之前,資源管理者會檢查資源池剩下的可用資源是否足夠。虛擬機器執行一段時間後,管理者會根據監測虛擬機器得到的數據,預測其行為,並釋出閒置的資源。透過預估可用的資源,我們的研究可以有效的利用雲端服務上的資源。
In recent years, cloud computing is a trend in the networking industry. By virtualization, we can optimize the usage of existing resources, simplify the management of infrastructure and software, and reduce hardware requirements. Our research focuses on Infrastructure as a Service (IaaS), pays attention to the allocation of resources from resource providers to resource consumers, and finds the optimization of system utilization. Before efficiently activating an additional Virtual Machine (VM) for some application, the system should check CPU usage in the resource pools. By monitoring VM’s CPU usage, each VM’s behavior can be estimated and idle resource can be released. Based on the estimation of available resource, the system determines allocation of VMs in the resource pools. Our proposed scheme can effectively dispatch VMs to maximize the utilization of the cloud computing center.
摘要............................................I
Abstract .......................................II
誌謝............................................III
Contents.......................................IV
List of Tables.................................VI
List of Figures................................VII
Chapter 1 Introduction............................1
1.1 OVERVIEW......................................1
1.2 MOTIVATION....................................2
1.3 CONTRIBUTION..................................2
1.4 ORGANIZATION ..................................3
Chapter 2 Related Work and Background.............4
2.1 CLOUD COMPUTING...............................4
2.2 THE SURVEY OF RESOURCE ALLOCATION.............6
2.3 RESOURCE MANAGEMENT WITH VSPHERE..............7
2.3.1 Resource Management.........................7
2.3.2 Resource Allocation Setting.................8
2.4 STATISTICAL METHOD............................9
2.4.1 Beta Distribution...........................9
2.4.2 Chi-Square Goodness-of-Fit Test............11
Chapter 3 Virtual Machine Allocation Problem.....13
3.1 DEFINITION AND ASSUMPTION....................13
3.1.1 Resource Providers, Consumers, and Manager.13
3.1.2 The Unit of Measurement....................14
3.1.3 Limit, Reservation, and Shares.............15
3.1.4 Available Resource and Overcommitted Usage.16
3.2 SCENARIO DESCRIPTION .........................17
3.2.1 Virtual Machine Allocation Problem .........17
3.2.2 Service Level Agreement....................19
3.3 BETA DISTRIBUTION AS CPU MODULE..............20
3.3.1 Assumption .................................20
3.3.2 Verification...............................21
3.3.3 Real Data with VMware vSphere..............22
Chapter 4 Simulation and Discussion..............29
4.1 COMBINE WITH THE SAME VMS TOGETHER...........29
4.2 DISCUSSION WITH CENTRAL LIMIT THEOREM........34
4.3 SIMULATION OF RESOURCE ALLOCATION PROBLEM....35
Chapter 5 Conclusion and Future Work.............40
References.......................................42

[1]P. Mell, et al, Cloud Computing: Recommendations of the National Institute of Standards and Technology, NIST, Spec. Pub. 800-145, Jan. 2011.
[2]E. Elghoneimy, O. Bouhali, H. Alnuweiri, Resource allocation and scheduling in cloud computing, Computing, Networking and Communications (ICNC), 2012 International Conference on, vol., no., pp.309-314, Jan. 30 2012-Feb. 2 2012.
[3]L. M. Vaquero, et al, Dynamically scaling applications in the cloud, SIGCOMM Comput. Commun. Rev., vol. 41, pp. 45-52, 2011.
[4]H. C. Lim, S. Babu, J. S. Chase, and S. S. Parekh, Automated control in cloud computing: challenges and opportunities, in ACDC ’09: Proceedings of the 1st workshop on Automated control for datacenters and clouds. ACM, pp. 13–18, 2009
[5]RightScale Inc., Web-based Cloud Computing Management Platform by RightScale. http://www. rightscale.com/.
[6]J. Kupferman, J. Silverman, P. Jara, and J. Browne, Scaling into the Cloud, http://cs.ucsb.edu/˜jkupferman/docs/ScalingIntoTheClouds.pdf, 2009.
[7]vSphere resource management guide
[8]Michael A, et al., Above the Clouds: A Berkeley View of Cloud Computing, EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2009-28, Feb. 2009.
[9]Amazon Elastic Compute Cloud (EC2). http://aws.amazon.com/ec2/.
[10]VMware: Virtualization via Hypervisor, Virtual Machine & Server Consolidation. http://www.vmware.com/.
[11]M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, M. Zaharia, A view of cloud computing, Communications of the ACM, Vol. 53 No. 4, Pages 50-58, April 2010.
[12]S. Chaisiri, B.-S. Lee, D. Niyato, Optimization of Resource Provisioning Cost in Cloud Computing, Services Computing, IEEE Transactions on, vol.5, no.2, pp.164-177, April-June 2012.
[13]Y. Sungkap, H.-H.S. Lee, Using Mathematical Modeling in Provisioning a Heterogeneous Cloud Computing Environment, Computer, vol.44, no.8, pp.55-62, Aug. 2011.
[14]J. Wu, L. Ping; X. Ge, Y. Wang, J. Fu, Cloud Storage as the Infrastructure of Cloud Computing, Intelligent Computing and Cognitive Informatics (ICICCI), 2010 International Conference on , vol., no., pp.380-383, 22-23 June 2010.
[15]M. Guazzone, C. Anglano, M. Canonico, Energy-Efficient Resource Management for Cloud Computing Infrastructures, Cloud Computing Technology and Science (CloudCom), 2011 IEEE Third International Conference on, vol., no., pp.424-431, Nov. 29 2011-Dec. 1 2011.
[16]S. S. Yadav, Z. W. Hua, CLOUD: A computing infrastructure on demand, Computer Engineering and Technology (ICCET), 2010 2nd International Conference on, vol.1, no., pp.V1-423-V1-426, 16-18 April 2010.
[17]B. Sotomayor, R. S. Montero, I. M. Llorente, I. Foster, Virtual Infrastructure Management in Private and Hybrid Clouds, Internet Computing, IEEE, vol.13, no.5, pp.14-22, Sept.-Oct. 2009.
[18]G. Wang, T. S. E. Ng, The Impact of Virtualization on Network Performance of Amazon EC2 Data Center, INFOCOM, 2010 Proceedings IEEE, vol., no., pp.1-9, 14-19 March 2010.
[19]R. Buyya, C. S. Yeo, S. Venugopal, Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as Computing Utilities, High Performance Computing and Communications, 2008. HPCC '08. 10th IEEE International Conference on, vol., no., pp.5-13, 25-27 Sept. 2008.
[20]L.-J. Zhang, Q. Zhou, CCOA: Cloud Computing Open Architecture, Web Services, 2009. ICWS 2009. IEEE International Conference on, vol., no., pp.607-616, 6-10 July 2009.
[21]R. Urgaonkar, U. C. Kozat, K. Igarashi, M. J. Neely, Dynamic resource allocation and power management in virtualized data centers, Network Operations and Management Symposium (NOMS), 2010 IEEE , vol., no., pp.479-486, 19-23 April 2010.

連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關期刊