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  1. NTU Theses and Dissertations Repository
  2. 管理學院
  3. 資訊管理學系
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/52158
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor林永松
dc.contributor.authorYu-Wen Chiouen
dc.contributor.author邱鈺雯zh_TW
dc.date.accessioned2021-06-15T16:08:47Z-
dc.date.available2020-08-25
dc.date.copyright2015-08-25
dc.date.issued2015
dc.date.submitted2015-08-19
dc.identifier.citation[1] Nan Ma, Mei Yuan, and Guoliang Cao, 2012, “Integration of Digital Campus Resources Based on Cloud Computing”, Proceedings of the 2012 International Conference on Cybernetics and Informatics Lecture Notes in Electrical Engineering, Volume 163, pp. 1957-1963.
[2] Chen Deng An, Wu Fu Chu, Cong Hong Ri, 2010, “Colleges and universities teaching resources of the network research”, Journals of Computer Engineering and Applications, Volume 27, pp. 167-173.
[3] Gartner Inc., 2012, “Hype Cycle for Cloud Computing, 2012”.
[4] Gartner Inc., 2012, “High-Tech Tuesday Webinar: Gartner Worldwide IT Spending Forecast, 2Q12 Update: Cloud Is the Silver Lining”.
[5] Gartner Releases Their Hype Cycle for Cloud Computing, 2012, http://www.business2community.com/tech-gadgets/gartner-releases-their-hype-cycle-for-cloud-computing-2012-0241167 (Accessed July 10, 2015)
[6] Amazon Elastic Compute Cloud (EC2), http://www.amazon.com/ec2/ (Accessed December 28, 2014).
[7] Google App Engine, http://appengine.google.com (Accessed December 28, 2014).
[8] Salesforce.com, http://www.salesforce.com/platform/ (Accessed December 28, 2014).
[9] Windows Azure Platform, http://www.microsoft.com/azure/ (Accessed December 28, 2014).
[10] Mayanka Katyal, Atul Mishra, December 2013, “A Comparative Study of Load Balancing Algorithms in Cloud Computing Environment”, International Journal of Distributed and Cloud Computing, Volume 1, Issue 2.
[11] Gartner Inc., May 2015, “Magic Quadrant for Cloud Infrastructure as a Service, Worldwide”.
[12] Nidhi Jain Kansal, Inderveer Chana, January 2012, “Cloud Load Balancing Techniques: A Step Towards Green Computing”, IJCSI International Journal of Computer Science Issues, Volume 9, Issue 1, No. 1, pp. 238-246.
[13] A. M. Alakeel, June 2010, “A Guide to dynamic Load balancing in Distributed Computer Systems”, International Journal of Computer Science and Network Security (IJCSNS), Volume 10, No. 6 , pp. 153-160.
[14] Amandeep, Vandana Yadav, Faz Mohammad, April 2014, “Different Strategies for Load Balancing in Cloud Computing Environment: a critical Study”, International Journal of Scientific Research Engineering & Technology (IJSRET), Volume 3, Issue 1, pp. 85-90.
[15] Michael Armbrust, Armando Fox, Rean Griffith, Anthony D. Joseph, Randy H. Katz, Andrew Konwinski, Gunho Lee, David A. Patterson, Ariel Rabkin, Ion Stoica, Matei Zaharia, February 10, 2009, “Above the Clouds: A Berkeley View of Cloud Computing”, Technical Report of Electrical Engineering and Computer Sciences University of California at Berkeley, No. UCB/EECS-2009-28.
[16] J. Brodkin, 2008, “Cloud computing hype spurs confusion, Gartner says,” http://www.computerworld.com/s/article/9115904/Cloud_computing_hype_spurs_confusion_Gartner_says (Accessed December 28, 2014).
[17] Peter Mell, Tim Grance, 2009, “Effectively and Securely Using the Cloud Computing Paradigm”, Information Technology Laboratory 6-2 by NIST.
[18] Han Qi, Abdullah Gani, 16-18 May 2012, “Research on mobile cloud computing: Review, trend and perspectives”, 2012 Second International Conference of Digital Information and Communication Technology and its Applications (DICTAP), pp. 195-202.
[19] Shuai Zhang, Shufen Zhang, Xuebin Chen, Xiuzhen Huo, 22-24 Jan. 2010, “Cloud Computing Research and Development Trend”, Future Networks 2010 ICFN '10. Second International Conference, pp. 93-97.
[20] CCRA Team and M. Buzetti, 2011, “Cloud Computing Reference, Architecture 2.0: Overview”, IBM Corporation.
[21] IBM Blue Cloud project,
http://www-03.ibm.com/press/us/en/pressrelease/22613.wss/
(Accessed December 28, 2014).
[22] Sean Marston a, Zhi Li, Subhajyoti Bandyopadhyay, Juheng Zhang, Anand Ghalsasi, Jan 2011, “Cloud computing - The business perspective”, 44th Hawaii International Conference of System Sciences (HICSS), Volume 51, Issue 1, pp. 1-11.
[23] A. Zahariev, 2009, “Google app engine”, Helsinki University of Technology.
[24] Lizhe Wang, Gregor von Laszewski, Andrew Younge, Xi He, Marcel Kunze, Jie Tao, Cheng Fu, April 2010, “Cloud Computing: a Perspective Study”, New Generation Computing, Volume 28, Issue 2, pp. 137-146.
[25] Independently conducted by Ponemon Institute, October 2014, “2014 Global Report on the Cost of Cyber Crime”, Sponsored by HP Enterprise Security.
[26] 趨勢科技,2013,”ART白皮書 2013”。
[27] Xun Xu, February 2012, “From cloud computing to cloud manufacturing”, Robotics and Computer-Integrated Manufacturing, Volume 28, Issue 1, pp. 75-86.
[28] 陳志華, 曾筱珽, 陳昱嘉, 林家瑜,2011,”雲端運算應用現況與發展趨勢”,證券櫃檯,頁34-45。
[29] Z. Zhang, and X. Zhang, May 2010, “A Load Balancing Mechanism Based on Ant Colony and Complex Network Theory in Open Cloud Computing Federation”, Proceedings of 2nd International Conference on Industrial Mechatronics and Automation (ICIMA) , pp. 240-243.
[30] M.L. Fisher, April 1985, “An Applications Oriented Guide to Lagrangian Relaxation,” Interfaces, Volume 15, No. 2, pp. 10-21.
[31] M.L. Fisher, January 1981, “The Lagrangian Relaxation Method for Solving Integer Programming Problems,” Management Science, Volume 27, No. 1, pp. 1-18.
[32] R.K. Ahuja, T.L. Magnanti, and J.B. Orlin, 1993, “Network Flows: Theory, Algorithms, and Applications: Chapter 16 Lagrangian Relaxation and Network Optimization,” Prentice-Hall, pp. 598-639.
[33] A. M. Geoffrion, 1974, “Lagrangean Relaxation and its Use in Integer Programming,” Mathematical Programming Study, Volume 2, pp. 82-114.
[34] Yao-Yuan Chang, 2010, “Network Defense and Recovery Strategies for Maximization of Network Survivability under Malicious Attacks”, Mater Thesis of Department of Information Management of National Taiwan University.
[35] Frank Yeong-Sung Lin, Jan 1998, “Quasi-static Channel Assignment Algorithms for Wireless Communications Networks,” Twelfth International Conference of Information Networking (ICOIN-12), pp. 434-437.
[36] Pisinger, D., 2003. “Where are the hard knapsack problems?” Technical Report, Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.
[37] Caccetta, L.; Kulanoot, A. (2001). 'Computational Aspects of Hard Knapsack Problems'. Nonlinear Analysis 47: 5547–5558.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/52158-
dc.description.abstract雲端運算在技術蓬勃發展下已愈趨成熟,各式各樣的應用類型與日俱增,近年來被廣泛地採用。對於企業客戶或使用者而言,雲端運算使其得以透過以量計價的方式租賃第三方提供的軟硬體資源,令使用者能將資金做更有效的運用,因此,使用者希望雲端服務提供商能滿足其對軟硬體資源之需求,並提供高的服務品質。對於雲端服務提供商而言,他們試圖最大化資源利用率及其可所獲取之利潤,換言之,亦即在有限的資源中,創造最大的利潤。為了使資源在雲端運算環境中有效的被利用,雲端服務提供商須採用合適之負載平衡技術。因此,如何提供良好品質的服務予使用者,並最大化其利潤,是雲端服務提供商最具挑戰性的問題之一。
本論文著重於協助雲端服務提供商在有限的資源中,使其利潤最大化,並將此問題數學模式化,進而發展出使用者允入控制演算法。在求解的過程中,採用拉格蘭日鬆弛法協助求得最佳解。
zh_TW
dc.description.abstractDue to the cloud computing has gained rapid adoption in recent years, cloud service providers have begun to establish new data centers to implements cloud computing application. For end users, cloud computing is a scenario that users can access any kind of infrastructures, applications or platforms in a cloud. Therefore, they hope cloud service providers can offer high quality cloud environment and satisfy their requirements. For cloud computing service providers, they try to maximize resource utilization and maximize their profits simultaneously. In order to make efficient use of resources in cloud computing systems and ensure their availability to the end users, they need to adopt a best suited load balance technique to achieve it. Hence, how to provide efficient cloud services to users and maximize the cloud service providers’ profits at the same time has become an extremely important issue.
In this thesis, we focus on helping cloud service provider to admit users as many as possible in the most efficient way. A generic mathematical programming model we proposed that can be used for developing an admission control and resources reallocation algorithm by simulating the role of the cloud service providers and users in cloud computing system. The Lagrangean relaxation method is adopted here to solve the problem mentioned above and obtain the optimal solutions.
en
dc.description.provenanceMade available in DSpace on 2021-06-15T16:08:47Z (GMT). No. of bitstreams: 1
ntu-104-R02725029-1.pdf: 1751277 bytes, checksum: 9ff4eff08c70602b68df0e3e5b3046d9 (MD5)
Previous issue date: 2015
en
dc.description.tableofcontents謝誌 I
Thesis Abstract III
論文摘要 V
Table of Contents VI
List of Figures VIII
List of Tables IX
Chapter1 Introduction 1
1.1 Background 1
1.2 Motivation 4
1.3 Literature Review 5
1.3.1 Features in Cloud Computing Systems 5
1.3.2 Trends for Cloud Computing Development 9
1.3.3 Load-Balance in Cloud Computing Systems 14
1.4 Thesis Organization 17
Chapter2 Problem Formulation 19
2.1 Problem Description 19
2.2 Problem Scenario 20
2.3 Mathematical Formulation 23
Chapter3 Solution Approach 30
3.1 Lagrangean Relaxation Method 30
3.2 Solution Approach for the Objective Function 34
3.2.1 Lagrangean Relaxation Problem 35
3.2.2 The Dual Problem and the Subgradient Method 39
3.2.3 Getting Primal Feasible Solutions 41
3.3 Summary of the Solution Approach for the Objective Function 46
Chapter4 Computational Experiment 48
4.1 Experiment Environment 48
4.2 Experiment Results of Drop and Add Strategy Combinations 54
4.2.1 Experiment Results 54
4.2.2 Discussion of Results 58
4.3 Experiment Results of Comparing LR with Lingo 60
4.3.1 Experiment Results 60
4.3.2 Discussion of Results 65
Chapter5 Conclusion and Future Work 67
5.1 Conclusion 67
5.2 Future Work 68
Reference 70
dc.language.isoen
dc.subject數學規劃zh_TW
dc.subject拉格蘭日鬆弛法zh_TW
dc.subject負載平衡zh_TW
dc.subject資源配置zh_TW
dc.subject最佳化zh_TW
dc.subject雲端運算zh_TW
dc.subjectCloud Computingen
dc.subjectLagrangean Relaxation Methoden
dc.subjectMathematical Programmingen
dc.subjectLoad Balanceen
dc.subjectOptimizationen
dc.subjectResource Allocationen
dc.title雲端運算系統中使雲端服務提供商利潤最大化之允入控制演算法zh_TW
dc.titleAn Admission Control Algorithm to Maximize Operator’s Profits in Cloud Computing Systemsen
dc.typeThesis
dc.date.schoolyear103-2
dc.description.degree碩士
dc.contributor.oralexamcommittee孔令傑,莊東穎,呂俊賢,鍾順平
dc.subject.keyword雲端運算,拉格蘭日鬆弛法,數學規劃,最佳化,資源配置,負載平衡,zh_TW
dc.subject.keywordCloud Computing,Lagrangean Relaxation Method,Mathematical Programming,Optimization,Resource Allocation,Load Balance,en
dc.relation.page75
dc.rights.note有償授權
dc.date.accepted2015-08-19
dc.contributor.author-college管理學院zh_TW
dc.contributor.author-dept資訊管理學研究所zh_TW
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