雲端平台之虛擬叢集管理與資源配置

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：93

、訪客IP：3.133.159.125

姓名

陳劭睿(Shao-Jui Chen) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

雲端平台之虛擬叢集管理與資源配置
(Virtual Clusters Management and Resource Allocation on Cloud Platform)

相關論文

★ 以伸展樹為基礎的Android Binder Driver	★ 應用增量式學習於多種農作物判釋之研究
★ 應用分類重建學習偵測航照圖幅中的新穎坵塊	★ 一個建立在平行工作系統上的動態全球計算平台
★ 用權重參照計數演算法執行主動物件垃圾收集	★ 一個動態負載平衡之最大可能性估算計算架構
★ 利用多項系統負載資訊進行動態P2P系統重組的策略研究	★ 基於Hadoop系統的雲端應用程式特徵擷取與計算監測架構
★ 適用於大型動態分散式系統的調適性計算模型	★ 一個提供彈性虛擬資料中心的雲端服務平台
★ 雲端彈性虛擬機房服務平台之資源控管中心	★ 一個適用於自動供應雲端系統的動態調適計算架構
★ 線性相關工作與非相關工作的探索式排程策略	★ 適用於大資料集高效率的分散式階層分群演算法
★ 混合雲端環境上的多重代理人動態調適計算管理架構	★ 基於圖形的平行化最小生成樹分群演算法

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

伺服器虛擬化(Server Virtualization)主要是利用虛擬化技術來讓實體機器承載多台虛擬機器，因此可以有彈性的使用計算資源。伺服器虛擬化的技術在雲端運算環境已被廣泛的使用，例如Amazon AWS與Microsoft Azure都是著名的商業運用的實例。然而隨著雲端運算的應用蓬勃發展，雲端運算的計算量與複雜度也隨著增加，單一虛擬機器已經無法符合使用者的需求，因此開始有虛擬叢集 (Virtual Cluster) 服務的需求產生。虛擬叢集服務允許使用者利用多台虛擬機器建置一個整合性的分散式運算環境，並且可以透過虛擬網路技術規畫所需的網路環境。然而因為不同的使用者有不同的虛擬叢集與網路環境需求，再加上雲端環境的虛擬叢集數量可能很龐大，因此不太可能讓雲端服務管理者逐一建置虛擬叢集以及進行資源配置。除此之外，虛擬叢集在運算過程中可能會需要虛擬機器彼此間進行協同運算，因此若要讓虛擬叢集的執行更有效率，進行資源配置時就需要考慮虛擬叢集的各項因素，而這些特點也導致傳統的虛擬機器資源配置機制在分配資源給虛擬叢集時無法達到好的效果。為了解決這個問題，本研究提出一套虛擬叢集管理工具，讓使用者透過網頁介面即可建置虛擬叢集。我們還提出了一個虛擬叢集資源管理機制，讓使用者依據其運算規模與安全性的需求，選擇進行運算時所需要的資源特性，再由系統自動進行資源配置與安全性設定。本研究亦提出的虛擬叢集監控機制，主要功能是分析虛擬叢集內部成員的網路運算關聯性，如此才能針對虛擬叢集成員之間關聯性進行有效的資源配置。

摘要(英)

Server virtualization is an important technology that enables various software-based VMs (VMs) running on a physical server simultaneously. Many existing commercial cloud services, such as Amazon AWS and Microsoft Azure, have already adopted this kind of technology because of its flexibility. The network virtualization technology is another key technology, which can create virtual network environments on top of the physical network environment. With server virtualization and network virtualization, the cloud users are able to create user-defined virtual clusters. Considering that the number of virtual clusters and the number of physical machines are usually large on a cloud, it is unrealistic to ask system administrators to do manual resource allocation and management. Instead, the users should be able to configure their virtual clusters, and the system should be responsible for allocating physical resources for the virtual clusters automatically. The challenge is, existing cloud systems/platforms, such as OpenStack, do not fully support deployment/management functions of virtual clusters. In addition, resource allocation for a virtual cluster using a traditional approach becomes inefficient because it is not aware of the relationship among the VMs of a virtual cluster. To this end, we have developed a cloud platform based on OpenStack, namely SAMEVED, to support various resource allocation/management functions of user-defined virtual clusters. We provide a web-based UI for end users, such that the end users can directly create and launch their own virtual clusters without the intervention of the system administrators. While creating a virtual cluster, the user can specify the network configuration and the security constraints of the virtual cluster. When a virtual cluster is set for deployment, the system should automatically place the VMs of a virtual cluster on physical machines based on the user requirement, such that the resource usage is optimized. The proposed resource allocation mechanism relies on a monitor to collect resource usage of a virtual cluster, as well as network consumption of each VM. Then it uses a profiler to classify the types of VMs. Finally the mechanism uses the processed information to place VMs on the right physical machines.

關鍵字(中)

★ 雲端運算
★ 虛擬叢集
★ 排程機制
★ 資源配置

關鍵字(英)

★ Cloud computing
★ Virtual Cluster
★ Virtual Cluster Placement
★ Resource allocation

論文目次

摘要 I
Abstract II
List of Figures VI
List of Table VIII
List of Algorithm VIII
Chapter 1. Introduction 1
1-1. Virtual Clusters on Cloud Computing 3
1-2. Existing VM Placement Strategies 6
1-3. Problem Definition 8
1-4. Contributions of Dissertation 10
1-5. Organization of Dissertation 11
Chapter 2. Background and Literature Review 12
2-1. VM/Cluster Management Tools and Related Technologies 12
2-2. Virtual Cluster on OpenStack 16
2-3. VMs/Cluster Deployment Strategy on Cloud Platform 17
2-3-1. Traditional Deployment Strategy for VMs 17
2-3-2. OpenStack Filter Scheduler 18
2-3-3. Network-aware embedding of virtual machine clusters onto federated cloud infrastructure 19
2-3-4. Resource Scheduling and Data Locality for Virtualized Hadoop on IaaS Cloud Platform 20
2-3-5. Detecting and Managing VM Ensembles in Virtualized Data Centers 21
2-3-6. HPC-Aware VM Placement in Infrastructure Clouds 23
2-3-7. Virtual Machine Provisioning Based on Analytical Performance and QoS in Cloud Computing Environments 23
Chapter 3. SAMEVED: A Cloud Platform for Virtual Cluster Management 24
3-1. Software Architecture of SAMEVED 24
3-2. SAMEVED Compute Controller 26
3-3. SAMEVED Network Controller 28
3-4. Profiling Monitor 30
3-5. SAMEVED API 32
3-6. Case Study: Cloud Security Experimental Platform over SAMEVED 38
Chapter 4. Network Topology-Aware Deployment for Virtual Clusters on SAMEVED 40
4-1. Overview 41
4-2. Concept of Network Topology-Aware of Virtual Cluster Deployment on SAMEVED 42
4-3. Problem Definition for Virtual Cluster Deployment 43
4-4. Naive Virtual Cluster Deployment Strategy 46
4-5. Deployment Strategy for Network-Aware Virtual Cluster Deployment 49
4-6. Experimental Results 52
4-7. Summary 68
Chapter 5. Extending SAMEVED on Citrix XenServer 69
5-1. System Overview of SAMEVED on Citrix XenServer 70
5-2. SAMEVED Frontend 72
5-3. SAMEVED Compute Controller 72
5-4. SAMEVED Watch 74
5-5. Summary 76
Chapter 6. Conclusions and Future Work 78
References 81

參考文獻

[1] K. Hwang, G. C. Fox, and J. J. Dongarra, Distributed and cloud computing : from parallel processing to the Internet of things. Amsterdam ; Boston: Morgan Kaufmann, 2012.
[2] I. Foster, Y. Zhao, I. Raicu, and S. Lu, ”Cloud Computing and Grid Computing 360-Degree Compared,” in 2008 Grid Computing Environments Workshop(GCE ′08), 2008, pp. 1-10.
[3] B. Furht and A. Escalante, Handbook of Cloud Computing: Springer Publishing Company, Incorporated, 2010.
[4] M. Armbrust, O. Fox, R. Griffith, A. D. Joseph, Y. Katz, A. Konwinski, et al. (2009). Above the clouds: A Berkeley view of cloud computing [Online]. Available: http://cacs.usc.edu/education/cs653/Armbrust-CloudComp-Berkeley09.pdf
[5] P. Mell and T. Grance, ”The NIST definition of cloud computing,” National Institute of Standards and Technology2011.
[6] J. R. Wernsing and G. Stitt, Elastic computing: a framework for transparent, portable, and adaptive multi-core heterogeneous computing vol. 45: ACM, 2010.
[7] J. W. Ross and G. Westerman, ”Preparing for utility computing: The role of IT architecture and relationship management,” IBM systems journal, vol. 43, pp. 5-19, 2004.
[8] D. Robinson, Amazon Web Services Made Simple: Learn how Amazon EC2, S3, SimpleDB and SQS Web Services enables you to reach business goals faster: Emereo Pty Ltd, 2008.
[9] D. Nurmi, R. Wolski, C. Grzegorczyk, G. Obertelli, S. Soman, L. Youseff, et al., ”The Eucalyptus Open-Source Cloud-Computing System,” IEEE/ACM International Symposium on Cluster Computing and the Grid, 2009., pp. 124-131, 2009.
[10] D. Milojicic and R. Wolski, ”Eucalyptus: Delivering a Private Cloud,” Computer, vol. 44, pp. 102-104, 2011.
[11] Eucalyptus-Elastic Utility Computing Architecture Linking Your Programs To Useful Systems. Available: https://docs.eucalyptus.com/eucalyptus/latest/
[12] Home • eucalyptus/eucalyptus Wiki • GitHub. Available: https://github.com/eucalyptus/eucalyptus/wiki
[13] P. Sempolinski and D. Thain, ”A Comparison and Critique of Eucalyptus, OpenNebula and Nimbus,” in 2010 IEEE Second International Conference on Cloud Computing Technology and Science, 2010, pp. 417-426.
[14] D. Milojičić, I. M. Llorente, and R. S. Montero, ”OpenNebula: A Cloud Management Tool,” IEEE Internet Computing, vol. 15, pp. 11-14, 2011.
[15] B. Sotomayor, R. S. Montero, I. M. Llorente, and I. Foster, ”Virtual Infrastructure Management in Private and Hybrid Clouds,” IEEE Internet Computing, vol. 13, pp. 14-22, 2009.
[16] M. Rosenblum and T. Garfinkel, ”Virtual Machine Monitors: Current Technology and Future Trends,” Computer, vol. 38, pp. 39-47, 2005.
[17] A. Muller, S. Wilson, D. Happe, G. J. Humphrey, and R. Troupe, Virtualization with VMware ESX Server: Syngress, 2005.
[18] S. Shirinbab, L. Lundberg, and D. Ilie, ”Performance comparison of kvm, vmware and xenserver using a large telecommunication application,” in Cloud Computing, 2014.
[19] D. E. Williams, Virtualization with Xen (tm): Including XenEnterprise, XenServer, and XenExpress: Syngress, 2007.
[20] T. Dillon, C. Wu, and E. Chang, ”Cloud Computing: Issues and Challenges,” in 24th IEEE International Conference on Advanced Information Networking and Applications, 2010, pp. 27-33.
[21] K. Hwang, J. Dongarra, and G. C. Fox, Distributed and cloud computing: from parallel processing to the internet of things: Morgan Kaufmann, 2013.
[22] X. Meng, V. Pappas, and L. Zhang, ”Improving the scalability of data center networks with traffic-aware virtual machine placement,” in Proceedings of the 29th conference on Information communications, San Diego, California, USA, 2010, pp. 1154-1162.
[23] H. N. Van, F. D. Tran, and J. M. Menaud, ”Autonomic virtual resource management for service hosting platforms,” in 2009 ICSE Workshop on Software Engineering Challenges of Cloud Computing, 2009, pp. 1-8.
[24] J. Xu and J. A. B. Fortes, ”Multi-Objective Virtual Machine Placement in Virtualized Data Center Environments,” in Proceedings of the 2010 IEEE/ACM Int′l Conference on Green Computing and Communications & Int′l Conference on Cyber, Physical and Social Computing, 2010, pp. 179-188.
[25] J. Hu, J. Gu, G. Sun, and T. Zhao, ”A Scheduling Strategy on Load Balancing of Virtual Machine Resources in Cloud Computing Environment,” in 2010 3rd International Symposium on Parallel Architectures, Algorithms and Programming, 2010, pp. 89-96.
[26] B. Hu, Z. Lei, Y. Lei, D. Xu, and J. Li, ”A TimeSeries Based Precopy Approach for Live Migration of Virtual Machine,” in 2011 IEEE 17th International Conference on Parallel and Distributed Systems (ICPADS), , 2011, pp. 947-952.
[27] C. Ghribi, M. Hadji, and D. Zeghlache, ”Energy Efficient VM Scheduling for Cloud Data Centers: Exact Allocation and Migration Algorithms,” in 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, 2013, pp. 671-678.
[28] Citrix XenServer. Available: https://xenserver.org/
[29] K. Jackson and C. Bunch, OpenStack Cloud Computing Cookbook - Second Edition, 2 edition ed. Birmingham, UK: Packt Publishing, 2013.
[30] T. White, Hadoop: The definitive guide: ” O′Reilly Media, Inc.”, 2012.
[31] M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. Katz, A. Konwinski, et al., ”A view of cloud computing,” Communications of the ACM vol. 53, pp. 50-58, 2010.
[32] K. Pepple, Deploying OpenStack: ”O′Reilly Media, Inc.”, 2011.
[33] O. SEFRAOUI, M. AISSAOUI, and M. ELEULDJ, ”OpenStack: Toward an Open-Source Solution for Cloud Computing,” International Journal of Computer Applications vol. 55, pp. 38-42, 2012.
[34] Y. Chen, C. Chuang, H. Liu, C. Ni, and C. Wang, ”Using Agent in Virtual Machine for Interactive Security Training,” Security Technology, pp. 65-74, 2011.
[35] P. Barham, B. Dragovic, K. Fraser, S. Hand, T. Harris, A. Ho, et al., ”Xen and the art of virtualization,” in Proceedings of the nineteenth ACM symposium on Operating systems principles, 2003, pp. 164-177.
[36] D. J. Protti, ”Linux KVM as a learning tool,” Linux Journal p. 3, 2009.
[37] S. Beco, A. Maraschini, F. Pacini, O. Biran, D. Breitgand, K. Meth, et al., ”Cloud computing and RESERVOIR project,” Nuovo Cimento C Geophysics Space Physics C, vol. 32, pp. 99-103, 2009.
[38] P. Knight and C. Lewis, ”Layer 2 and 3 virtual private networks: taxonomy, technology, and standardization efforts,” IEEE Communications Magazine, vol. 42, pp. 124-131, 2004.
[39] N. Basil, A. Srikanth, and T. Meehan, ”Determining an end point of a GRE tunnel,” ed: U.S. Patent No. 6,779,051, 2004.
[40] B. A. A. Nunes, M. Mendonca, X. N. Nguyen, K. Obraczka, and T. Turletti, ”A Survey of Software-Defined Networking: Past, Present, and Future of Programmable Networks,” IEEE Communications Surveys & Tutorials, vol. 16, pp. 1617-1634, 2014.
[41] N. McKeown, T. Anderson, H. Balakrishnan, G. Parulkar, L. Peterson, J. Rexford, et al., ”OpenFlow: enabling innovation in campus networks,” ACM SIGCOMM Computer Communication Review, vol. 38, pp. 69-74, 2008.
[42] B. Pfaff, J. Pettit, T. Koponen, E. J. Jackson, A. Zhou, J. Rajahalme, et al., ”The Design and Implementation of Open vSwitch,” in Proceedings of the 12th USENIX Conference on Networked Systems Design and Implementation, 2015, pp. 117-130.
[43] D. T. Bui and K. Aberkane, ”A generic interface for Open vSwitch,” in 2016 IEEE NetSoft Conference and Workshops (NetSoft), 2016, pp. 53-57.
[44] Sahara - OpenStack. Available: https://docs.openstack.org/sahara/latest/
[45] A. G. Shoro and T. R. Soomro, ”Big data analysis: Apache spark perspective,” Global Journal of Computer Science and Technology, vol. 15, 2015.
[46] Apache Spark™ - Lightning-Fast Cluster Computing. Available: https://spark.apache.org/
[47] A. Ibrahim and M. EL-NAWAWY, ”A study of adopting big data to cloud computing,” in International Association for Management of Technology, The Westin, Cape Town,South Africa, 2015, pp. 1-7.
[48] M. Baker, ”Cluster Computing White Paper,” arXiv:cs/0004014, 2000.
[49] S. Chen, C. Chen, H. Lu, and W. Wang, ”Efficient Resource Provisioning for Virtual Clusters on the Cloud,” in 2015 International Conference on Platform Technology and Service, 2015.
[50] R. Buyya, A. Beloglazov, and J. Abawajy, ”Energy-efficient management of data center resources for cloud computing: A vision, architectural elements, and open challenges,” in Proceedings of the 2010 International Conference on Parallel and Distributed Processing Techniques and Applications, 2010.
[51] D. Jiang, P. Huang, P. Lin, and J. Jiang, ”Energy efficient VM placement heuristic algorithms comparison for cloud with multidimensional resources,” in International Conference on Information Computing and Applications, 2012, pp. 413-420.
[52] R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. De Rose, and R. Buyya, ”CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms,” Software: Practice and Experience, vol. 41, pp. 23-50, 2011.
[53] Z. Zhang, H. Wang, L. Xiao, and L. Ruan, ”A statistical based resource allocation scheme in cloud,” in 2011 International Conference on Cloud and Service Computing (CSC), 2011, pp. 266-273.
[54] Z. Zhang, L. Xiao, Y. Li, and L. Ruan, ”A VM-based resource management method using statistics,” in 2012 IEEE 18th International Conference on Parallel and Distributed Systems (ICPADS), 2012, pp. 788-793.
[55] K. H. Kim, A. Beloglazov, and R. Buyya, ”Power‐aware provisioning of virtual machines for real‐time Cloud services,” Concurrency and Computation: Practice and Experience, vol. 23, pp. 1491-1505, 2011.
[56] A. Aral and T. Ovatman, ”Network-aware embedding of virtual machine clusters onto federated cloud infrastructure,” Journal of Systems and Software, vol. 120, pp. 89-104, 2016.
[57] D. Tao, B. Wang, Z. Lin, and T.-Y. Wu, ”Resource Scheduling and Data Locality for Virtualized Hadoop on IaaS Cloud Platform,” in Proceedings of Big Data Computing and Communications: Second International Conference, BigCom 2016, 2016, pp. 332-341.
[58] D. Tao, Z. Lin, and B. Wang, ”Load feedback-based resource scheduling and dynamic migration-based data locality for virtual hadoop clusters in openstack-based clouds,” Tsinghua Science and Technology, vol. 22, pp. 149-159, 2017.
[59] L. Hu, K. Schwan, A. Gulati, J. Zhang, and C. Wang, ”Net-cohort: detecting and managing VM ensembles in virtualized data centers,” in Proceedings of the 9th international conference on Autonomic computing, 2012, pp. 3-12.
[60] A. Gupta, L. V. Kale, D. Milojicic, P. Faraboschi, and S. M. Balle, ”HPC-Aware VM Placement in Infrastructure Clouds,” in Proceedings of the 2013 IEEE International Conference on Cloud Engineering, 2013, pp. 11-20.
[61] R. N. Calheiros, R. Ranjan, and R. Buyya, ”Virtual Machine Provisioning Based on Analytical Performance and QoS in Cloud Computing Environments,” in 2011 International Conference on Parallel Processing, 2011, pp. 295-304.
[62] T. A. Xavier and R. Rejimoan, ”Survey on various resource allocation strategies in cloud,” in 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT), 2016, pp. 1-4.
[63] S. Huang, J. Huang, J. Dai, T. Xie, and B. Huang, ”The HiBench benchmark suite: Characterization of the MapReduce-based data analysis,” in 2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW), , 2010, pp. 41-51.
[64] Iperf - The TCP/UDP Bandwidth Measurement Tool. Available: https://iperf.fr/
[65] E. Cecchet, J. Marguerite, and W. Zwaenepoel, ”Performance and scalability of EJB applications,” in Proceedings of the 17th ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications, 2002, pp. 246-261.
[66] E. Cecchet, A. Chanda, S. Elnikety, J. Marguerite, and W. Zwaenepoel, ”A comparison of software architectures for e-business applications,” in Procceeding of 4th Middleware Conference, 2002.
[67] S.-J. Chen, J.-Y. Huang, C.-T. Huang, and W.-J. Wang, ”SAMEVED: A System Architecture for Managing and Establishing Virtual Elastic Datacenters,” Int. J. Grid High Perform. Comput., vol. 5, pp. 27-42, 2013.

指導教授

王尉任(Wei-Jen Wang)

審核日期

2017-8-23

推文