研究生: |
林鉉竣 Lin, Hsuan-Chun |
---|---|
論文名稱: |
設計與模擬使用者到達變異性為基礎的雲端異質性虛擬機器資源提供與指派方法 Design and Modeling of Arrival Variance-based Methods for Resource Provision and Allocation Management with Heterogeneous Types of VMs in Cloud Computing Systems |
指導教授: |
陳朝鈞
Chen, Chao-Chun |
學位類別: |
碩士 Master |
系所名稱: |
電機資訊學院 - 製造資訊與系統研究所 Institute of Manufacturing Information and Systems |
論文出版年: | 2014 |
畢業學年度: | 102 |
語文別: | 中文 |
論文頁數: | 46 |
中文關鍵詞: | 雲端運算 、資源提供策略 、隨機派翠網 、虛擬機器比例 |
外文關鍵詞: | cloud computing, Resource management strategies, Stochastic Petri Nets, the proportion of the virtual machine |
相關次數: | 點閱:108 下載:2 |
分享至: |
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
雲端運算是近年來成長快速且受歡迎的新興服務遞送模式。雲端供應商可以在特定的時間將配有硬體資源(resources)、開發平台(Development Platform)或應用程式服務(Application Service)的虛擬機器(Virtual Machine, VM)指派給使用者。
提供這些服務的雲端供應商可以用服務類型分成三類:
(1)提供商業軟體當作服務(Software as a Service; SaaS)
(2)提供開發平台為服務(Platform as a service; PaaS)
(3)提供硬體資源當服務(Snfrastructure as a service; IaaS)並以隨支隨付(Pay as you use)的付費方式來計算出租雲端服務的使用費用 [20]。
目前在各種類型的雲端服務有許多知名廠商積極投入。 SaaS供應商為達成平均總成本支出的目標,需要在不同環境參數下決定資源池規模大小與組成資源池的虛擬機器種類。資源池規模大小與組成資源池機器種類將決定SaaS供應商的租賃成本與違背成本。 SaaS供應商為根據使用者數量,將資源池中不同種類的虛擬機器數量擴張或收縮以期達到有效之資源提供方式。
本研究主要目的是在隨機的使用者需求下,根據平均使用者抵達人數與需求波動大小將決定資源池(resource pool)內虛擬機器種類間的比例,並達到雲端供應商支付服務運行成本(租賃成本加違約成本)最小化的目標。
本研究將此問題分成兩個階段(Phase)進行解題:
在第一階段,本研究根據平均使用者抵達人數與需求波動大小決定資源池初始規模大小。
在第二階段,延續第一階段的資源池規模大小,根據使用者等候狀態調整建構資源池中虛擬機器的比例。
本研究針對上述步驟建立隨機派翠網模型(Stochastic Petri Nets, SPN)評估雲端資源提供策略並設計三個實驗佐證我們的想法是正確的
(1)同預期人數不同抵達變異程度對機台類型比例的影響
(2)用多種虛擬機器組合資源池比單一虛擬機器有較低的成本
(3)變異驅動資源指派策略和 optimal multiserver approach比較。
關鍵字:雲端運算、資源提供策略、隨機派翠網、虛擬機器比例
In this paper, we discuss how to decide SaaS vendor resources size and type under different user behavior, lead to average total cost minimum.
We propose ``Arrival Variance-based Methods for Resource Provision and Allocation Management with Heterogeneous Types of VMs' according to the average user arrivals determine the size of the resource pool and virtual machines type ratio within cloud service providers to achieve goal.
We designed a series of experiments to support our idea is correct and experiments results show our methods is significant.
[1] abiquo. [Online]. Available: http://www.abiquo.com/.
[2] accelops. [Online]. Available: http://www.accelops.com/.
[3] Amazon ec2. [Online]. Available: http://aws.amazon.com/cn/ec2/.
[4] appdynamics. [Online]. Available: http://www.appdynamics.com/.
[5] Appistry. [Online]. Available: http://www.appistry.com/.
[6] Appscale. [Online]. Available: http://www.appscale.com/.
[7] At&t cloud 101. [Online]. Available: https://www.synaptic.att.com/
clouduser/html/cloud101/Cloud_101_Details.htm.
[8] Bluelock. [Online]. Available: http://www.bluelock.com/.
[9] Chelsio communications. [Online]. Available: http://www.chelsio.com/.
[10] Einstein@home. [Online]. Available: http://www.einstein-online.info/
spotlights/EaH.
[11] How to scale an application. [Online]. Available: http://www.windowsazure.
com/en-us/documentation/articles/cloud-services-how-to-scale/.
[12] Hp performance optimized datacenter. [Online]. Available: http://www.ndm.net/
hppod/.
[13] Openstack. [Online]. Available: http://www.openstack.org/.
[14] Oracle’s on demand crm software. [Online]. Available: http://www.oracle.com/
us/products/applications/crmondemand/index.html.
[15] Salesforce. [Online]. Available: http://www.salesforce.com/tw/?ir=1.
[16] Sap er. [Online]. Available: http://www.sacom/index.html.
[17] Smartcloud enterprise. [Online]. Available: http://www-935.ibm.com/
services/us/en/cloud-enterprise/index.html.
[18] workday. [Online]. Available: http://www.workday.com/ap/.
[19] D. Ardagna and M. Passacantando. Generalized nash equilibria for the service provisioning problem in cloud systems. IEEE Transactions on Service Computing, 6:429–442, 2013.
[20] M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, and M. Zaharia. A view of cloud computing. In Communications of the ACM, volume 53, pages 50–58. April 2010.
[21] S. Aulbach, T. Grust, D. Jacobs, A. Kemper, and J. Rittinger. Multi-tenant databases for software as a service: Schema-mapping techniques. ACM SIGMOD International Conference on Management of Data, pages 1195–1206, 2008.
[22] A. Beloglazov, J. Abawajy, and R. Buyya. Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Generation Computer Systems, 28:755–768, 2012.
[23] A. Benlian, M. Koufaris, and T. Hess. Service quality in software-as-a-service: Developing the saas-qual measure and examining its role in usage continuance. Journal of Management Information Systems, 8:85–126, 2011.
[24] D. Bruneo. A stochastic model to investigate data center performance and qos in iaas cloud computing systems. IEEE Transactions on Parallel and Distributed System, 25:560–569, 2014.
[25] M. Bulmer. Principles of Statistics. Dover Publications, 1979.
[26] J. M. Butler, W. Theilmann, and R. Yahyapour. Service Level Agreements for Cloud Computing. Springer, 2011.
[27] J. Cao, K. Hwang, K. Li, and A. Y. Zomaya. Optimal multiserver configuration for profit maximization in cloud computing. IEEE Transactions on Parallel and Distributed System, 24:1087–1096, 2013.
[28] A. Castro, V. Villagra, B. Fuentes, and B. Costales. A flexible architecture for service management in the cloud. IEEE Transactions on Network and Service Management, 11:116–125, 2014.
[29] C. E. Catlett. In search of gigabit applications. In IEEE Communications Magazine. IEEE, 1992.
[30] S. Chaisiri, B.-S. Lee, , and D. Niyato. Optimization of resource provisioning cost in cloud computing. IEEE Transactions on Service Computing, 5(2):164–177, 2012.
[31] G. Ciardo, R. German, and C. Lindemann. A characterization of the stochastic process underlying a stochastic petri net. IEEE Transactions on Software Engineering, 20:506–515, 1994.
[32] Y. Dong, Y. Xia, Q. Zhu, and Y. Huang. A stochastic approach to predict performance of web service composition. In International Symposium on Electronic Commerce and Security, pages 460–464. IEEE, 2009.
[33] J. Du, D. J. Dean, Y. Tan, X. Gu, and T. Yu. Scalable distributed service integrity attestation for software-as-a-service clouds. IEEE Transactions on Parallel and Distributed System, PP(99), 2013.
[34] T. Genez, L. Bittencourt, and E. Madeira. Workflow scheduling for saas / paas cloud providers considering two sla levels. In Network Operations and Management Symposium (NOMS), pages 906–912. IEEE, 2012.
[35] C. Gravier, J. Subercaze, A. Najjar, F. Laforest, X. Serpaggi, and O. Boissier. Context awareness as a service for cloud resource optimization. IEEE Internet Computing, PP:1089–7801, 2013.
[36] C. Hirel, B. Tuffin, and K. Trivedi. Spnp: Stochastic petri nets. version 6.0. In B. Haverkort, H. Bohnenkamp, and C. Smith, editors, Computer Performance Evaluation.Modelling Techniques and Tools, volume 1786, pages 354–357. Springer Berlin Heidelberg, 2000.
[37] Z. Huang, C. He, L. Gu, and J. Wu. On-demand service in grid: Architecture, design and implementation. In International Conference on Parallel and Distributed Systems. IEEE Computer Society, 2005.
[38] A. Iosup, S. Ostermann, M. N. Yigitbasi, R. Prodan, T. Fahringer, and D. H. Epema. Performance analysis of cloud computing services for many-tasks scientific computing. IEEE Transactions on Parallel and Distributed System, 22:931–945, 2011.
[39] L. Jianjie, H. Zhaohui, Y. Xuan, Z. Ran, and X. Chengan. Analysis of process of triage in disaster rescue action using stochastic petri net. In Industrial Control and Electronics Engineering, pages 111–115. IEEE, 2012.
[40] M. Jo, Y. W. Ahn, A. Cheng, J. Baek, and H.-H. Chen. An auto-scaling mechanism for virtual resources to support mobile, pervasive, real-time healthcare applications in cloud computing. IEEE Network, 27:62–68, 2013.
[41] E. C. Jr, A. Puhalskii, M. Reiman, and E. Wright. Processor-shared buffers with reneging. Performance Evaluation, 19:26 –46, 1994.
[42] E. Kao. A semi-markovian population model with application to hospital planning. IEEE Transactions on Systems Man and Cybernetics, 3:327–336, 1973.
[43] L. Lei, Y. Han, and Z. Zhong. Performance analysis of device-to-device communications with frequency reuse using stochastic petri nets. In Wireless Networking Symposium, pages 6354–6359. IEEE, 2013.
[44] H. Liang, L. X. Cai, D. Huang, X. Shen, and D. Peng. An smdp-based service model for interdomain resource allocation in mobile cloud networks. IEEE Transactions on Vehicular Technology, 61:2222–2232, 2012.
[45] H. Liao. Saas business model for software enterprise. In Information Management and Engineering (ICIME), pages 604–607. IEEE, 2010.
[46] C. LIN and D. C. Marinescu. Stochastic high-level petri nets and applications. IEEE Transactions on Computers, 37:815–823, 1998.
[47] F. Liu, B. Li, Z. Zhou, B. Li, H. Jin, and H. Jiang. On arbitrating the powerperformance tradeoff in saas clouds. IEEE Transactions on Parallel and Distributed System, PP(99):1, 2013.
[48] D. Ma. The business model of software-as-a-service. In International Conference on Services Computing. IEEE, 2007.
[49] Z. Ma, P. E. Caines, and R. Malhame. Control of admission and routing in loss networks: Hybrid dynamic programming equations. IEEE Transactions on Automatic Control, 55:350–366, 2010.
[50] M. Mao and M. Humphrey. A performance study on the vm startup time in the cloud. In International Conference on Cloud Computing, pages 423–430. IEEE, 2012.
[51] P. Mell and T. Grance. The nist definition of cloud computing. Technical Report Special Publication 800-145, National Institute of Standards and Technology U.S. Department of Commerce, September 2011.
[52] Y. Mo, J. Chen, X. Xie, C. Luo, and L. T. Yang. Cloud-based mobile multimedia recommendation system with user behavior information. Ieee Systems Journal, 8:184–193, 2014.
[53] S. Namasivayam. Profiting from business process outsourcing. In IT Pro. IEEE Computer Society, 2004.
[54] V. Nikolopoulos, G. Mpardis, I. Giannoukos, I. Lykourentzou, and V. Loumos.
Web-based decision-support system methodology for smart provision of adaptive digital energy services over cloud technologies. IET Software, 5:454–465, 2011.
[55] Z. Pervez, S. Lee, and Y.-K. Lee. Multi-tenant, secure, load disseminated saas architecture. In International Conference on Advanced Communication Technology, pages 214–219. IEEE, 2010.
[56] B. Silva, P. Maciel, J. Brilhante, and A. Zimmermann. Geoclouds modcs: A perfomability evaluation tool for disaster tolerant iaas clouds. In Systems Conference. IEEE, 2014.
[57] H. Sun, S. Huang, Y. Fan, and W. Su. Configuration and optimization of virtual business in cloud computing environment. In International Conference on Cloud and Green Computing. IEEE, 2012.
[58] W. Tan and M. Zhou. Business and Scientific Workflows: A Web Service-Oriented Approach. Wiley-IEEE Press, 2013.
[59] J. Tang, W. P. Tay, and Y. Wen. Dynamic request redirection and elastic service scaling in cloud-centric media networks. IEEE Transactions on Multimedia, PP:1–13, 2014.
[60] R. Uhlig, G. Neiger, D. Rodgers, A. L.Santoni, F. C. Martins, A. V. Anderson, S. M.Bennett, A. K. F. H, and L. L. Smith. Intel virtualization technology. In IEEE Computer Society. IEEE, 2005.
[61] J. Walz and D. Grier. Time to push the cloud. IT Professional, 12:14–16, 2010.
[62] L. Wu, S. K. Garg, S. Versteeg, and R. Buyya. Sla-based resource provisioning for software-as-a-service applications in cloud computing environments. IEEE Tarnsactions on Services Computing, pp:1–30, 2013.
[63] Z. Xiao, W. Song, and Q. Chen. Dynamic resource allocation using virtual machine for cloud computing environment. IEEE Transactions on Parallel and Distributed System, 24(6):1107–1117, 2013.
[64] B. Yang and L. Wei-Hong. Capability evaluation of air cargo export handling system using stochastic petri net. In Logistics Systems and Intelligent Management, pages 1583–1593. IEEE, 2010.
[65] S.-T. Yee and J. A. Ventura. Phase-type approximation of stochastic petri nets for analysis of manufacturing systems. IEEE Transactions on RoboticsA and automation, 16:318–322, 2000