基於虛擬網路通訊強度之雲端虛擬叢集排程策略

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姓名

王皓平(Hao-Ping Wang) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

基於虛擬網路通訊強度之雲端虛擬叢集排程策略
(Using Communication Intensiveness of Virtual Networks for Virtual Cluster Placement on IaaS Cloud)

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摘要(中)

雲端運算近年來的發展越趨成熟，除了帶給人們生活上更多的便利，也改變了傳統應用程式的架構和提供軟體服務的模式。然而，在不為一般人所知的背後，這些雲端計算資源的管理與分配正是驅動軟體服務架構改變，並讓人們生活更加便利。也因此，雲端環境中的資源分配問題是值得研究的議題，尤其針對由許多虛擬機器與虛擬網路所構成的虛擬叢集 (Virtual Cluster)資源分配問題更是具有挑戰性。現今的雲端平台資源排程器大多將這些屬於相同虛擬叢集的虛擬機器視為單獨的虛擬機器而無法察覺他們之間的關聯，因此在資源使用上可能比較沒有效率。此外，虛擬叢集的資源管理機制需要去解決一個重要的問題，也就是當平台上的虛擬叢集網路流量過大時，平台所提供之服務的品質會嚴重被影響。因此，本實驗開發了新一代的虛擬叢集排程器─ Insight Scheduler來改善虛擬叢集使用過多網路頻寬時導致服務品質降低的情形並增加資源分配的效率。Insight Scheduler透過資源監控與分析找出虛擬叢集的資源使用特性，再利用這些資訊對虛擬叢集的網路架構作優化，最終達成降平台整體網路流量以及更佳的資源分配效率等兩個目標。我們透過多個實驗並輔以數據、圖表，證實我們的虛擬叢集排程機制可優化大網路流量之虛擬叢集資源配置以達到降低網路流量達85%，與增加資源配置效率16%。

摘要(英)

The cloud computing paradigm becomes increasingly popular as more and more applications and services, running on various cloud data centers, are provided based on the paradigm. The use of cloud computing technology provides more flexibility and convenience to people’s daily lives, even though the cloud users do not know how the enabling technologies work in a datacenter. One of the key enabling technologies is virtualization, which enables the use of virtual machines with virtual networks running on physical machines. With server virtualization and network virtualization, users are able to create user-defined virtual clusters to host their applications. In most cases, some VMs of a virtual cluster may frequently access each other frequently. As a result, many virtual clusters are communication-intensive in practice. The problem of virtual cluster placement is considered more complicated than VM placement since the communication-intensiveness of VMs has to be considered. In addition, existing VM placement (or scheduling) mechanism cannot handle this problem well. In this study, we have to proposed a new virtual cluster placement strategy, namely the Insight Scheduler. The proposed scheduler not only lowers the bandwidth consumption caused by the virtual clusters, but also achieves efficient resource provisioning. The proposed scheduler 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 scheduler uses the processed information to place VMs on physical machines. Based on our experimental results, the Insight Scheduler can reduce the physical network load by 85%, and increase application efficiency by 16%.

關鍵字(中)

★ 雲端計算
★ 虛擬叢集
★ 排程機制

關鍵字(英)

★ Cloud computing
★ OpenStack
★ Virtual Cluster Placement
★ Scheduling

論文目次

摘要 iv
Abstract v
目錄 vi
圖目錄 ix
表目錄 xi
第一章緒論 1
1-1 前言 1
1-2 問題定義與實作目標 3
1-3 論文貢獻 5
1-4 論文架構 6
第二章相關研究 7
2-1 背景知識 7
2-1-1 虛擬叢集 7
2-1-2 虛擬網路基礎設施 7
2-1-3 資料中心的架構與問題 10
2-2 相關系統 11
2-2-1 OpenStack 11
2-2-2 Sahara 12
2-2-3 SAMEVEDStack與CSEP 13
2-3 相關排程策略 14
2-3-1 Filter Scheduler 14
2-3-2 SAMEVEDStack Scheduler 15
2-4 找出Virtual Cluster之方法 16
第三章系統設計 18
3-1 系統架構 18
3-1-1 SAMEVEDStack Controller 19
3-1-2 Insight Scheduler 20
3-1-3 SAMEVEDStack Profiling Monitor 21
3-1-4 其他元件 23
3-2 系統運作流程 23
3-2-1 定義虛擬叢集與實體機器 23
3-2-2 調整虛擬叢集內部拓樸 24
3-2-3 對映虛擬叢集 25
3-3 虛擬叢集排程策略 26
第四章實驗環境與量測 30
4-1 實驗環境與情境假設 30
4-2 實驗案例 31
4-3 實驗結果與討論 33
4-3-1 實驗一：網路流量 33
4-3-2 實驗二：資源分配效率 36
第五章結論 39
第六章未來研究方向 40
6-1 動態虛擬叢集排程 40
6-2 多租戶虛擬叢集排程 40
參考資料 42

參考文獻

[1] M. Armbrust, O. Fox, R. Griffith, A. D. Joseph, Y. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, and others, “Above the clouds: A Berkeley view of cloud computing,” 2009.

[2] R. Buyya, J. Broberg, and A. M. Goscinski, Eds., Cloud Computing: Principles and Paradigms, 1 edition. Hoboken, N.J: Wiley, 2011.

[3] I. Foster, Yong Zhao, I. Raicu, and Shiyong Lu, “Cloud Computing and Grid Computing 360-Degree Compared,” Grid Comput. Environ. Workshop 2008 GCE 08, pp. 1–10, Nov. 2008.

[4] P. Mell and T. Grance, “The NIST definition of cloud computing,” National Institute of Standards and Technology, Gaithersburg,Maryland, Special Publication 800-145, 2011.

[5] R. P. Goldberg, “Survey of virtual machine research,” Computer, vol. 7, no. 9, pp. 34–45, 1974.

[6] 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,” SIGOPS Oper Syst Rev, vol. 37, no. 5, pp. 164–177, 2003.

[7] A. Kivity, Y. Kamay, D. Laor, U. Lublin, and A. Liguori, “kvm: the Linux virtual machine monitor,” presented at the Ottawa Linux Symposium, 2007, pp. 225–230.

[8] Al Muller, Seburn Wilson, Don Happe, Gary J. Humphrey, and Ralph Troupe, Virtualization with VMware ESX Server. Syngress, 2005.

[9] K. Pepple, Deploying OpenStack. O’Reilly Media, Inc., 2011.

[10] S.-J. Chen, C.-C. Chen, H.-L. Lu, and W.-J. Wang, “Efficient Resource Provisioning for Virtual Clusters on the Cloud,” presented at the 2015 International Conference on Platform Technology and Service, Jeju, Korea, 2015.

[11] M. Baker, “Cluster Computing White Paper,” arXiv:cs/0004014, Apr. 2000.

[12] G. F. Pfister, In Search of Clusters (2Nd Ed.). Upper Saddle River, NJ, USA: Prentice-Hall, Inc., 1998.

[13] J. Xie, S. Yin, X. Ruan, Z. Ding, Y. Tian, J. Majors, A. Manzanares, and X. Qin, “Improving MapReduce performance through data placement in heterogeneous Hadoop clusters,” in 2010 IEEE International Symposium on Parallel Distributed Processing, Workshops and Phd Forum (IPDPSW), 2010, pp. 1–9.

[14] I. Giurgiu, C. Castillo, A. Tantawi, and M. Steinder, “Enabling Efficient Placement of Virtual Infrastructures in the Cloud,” in Proceedings of the 13th International Middleware Conference, New York, NY, USA, 2012, pp. 332–353.

[15] 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 Commun. Surv. Tutor. Rev.

[16] 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, San Jose, California, USA, 2012, pp. 3–12.

[17] L. P. Cordella, P. Foggia, C. Sansone, and M. Vento, “A (sub)graph isomorphism algorithm for matching large graphs,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 10, pp. 1367–1372, Oct. 2004.

指導教授

王尉任

審核日期

2015-8-3

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