Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/19934
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dc.contributor曾學文zh_TW
dc.contributor.author張天釋zh_TW
dc.contributor.authorChang, Tien-Shihen_US
dc.contributor.other資訊科學與工程學系所zh_TW
dc.date2013en_US
dc.date.accessioned2014-06-06T07:07:59Z-
dc.date.available2014-06-06T07:07:59Z-
dc.identifierU0005-1308201321394500en_US
dc.identifier.citation[1] Gunjan Khanna, Kirk Beaty, Gautam Kar, Andrzej Kochut, “Application Performance Management in Virtualized Server Environments,” Proceedings of the IEEE/IFIP Network Operations and Management Symposium, pages 373-381, 2006. [2] AkshatVerma, Gargi Dasgupta, Tapan Kumar Nayak, Pradipta De, Ravi Kothari, “Server Workload Analysis for Power Minimization using Consolidation,” Proceedings of the USENIX Annual technical conference, pages 28-28, 2009. [3] Xiaoqiao Meng, Canturk Isci, Jeffrey Kephart, Li Zhang, Eric Bouillet, “Efficient Resource Provisioning in Compute Clouds via VM Multiplexing,” Proceedings of the International Conference on Autonomic computing, pages 11-20, 2010. [4] Kishaloy Halder, Umesh Bellur, Purushottam Kulkarni, “Risk Aware Provisioning and Resource Aggregation based Consolidation of Virtual Machines,” Proceedings of the IEEE International Conference on Cloud Computing, pages 598-605, 2012. [5] Jian Wan, Fei Pan, Congfeng Jiang, “Placement Strategy of Virtual Machines Based on Workload Characteristics,” Proceedings of the IEEE IPDPSW, pages 2140-2145, 2012. [6] Rajeshwari Ganesan, Santonu Sarkar, Akshay Narayan, “Analysis of SaaS Business Platform Workloads for Sizing and Collocation,” Proceedings of the IEEE International Conference on Cloud Computing, pages 868-875, 2012. [7] Geetika Goel, Rajeshwari Ganesan, Santonu Sarkar, Kavish Kaup, “iCirrus Wop: Workload Analysis for Virtual Machine Placements,” Proceedings of the IEEE International Conference on Parallel and Distributed Systems, pages 732-737, 2012. [8] Ming Chen, Hui Zhangt, Ya-Yunn Su, Xiaorui Wang, Guofei Jiangt, Kenji Yoshihirat, “Effective VM Sizing in Virtualized Data Centers,” Proceedings of the IFIP/IEEE International Symposium on Integrated Network Management, pages 594-601, 2011. [9] Bipin B. Nandi, Ansuman Banerjee, Sasthi C. Ghosh, Nilanjan Banerjee, “Stochastic VM Multiplexing for Datacenter Consolidation,” Proceedings of the IEEE International Conference on Services Computing, pages 114-121, 2012. [10] Balaji Viswanathan, Akshat Verma, Sourav Dutta, "CloudMap: Workload-aware Placement in Private Heterogeneous Clouds," Proceedings of the IEEE Network Operations and Management Symposium, pages 9-16, 2012. [11] Jenn-Wei Lin, Chien-Hung Chen, “Interference-aware virtual machine placement in cloud computing systems,” Proceedings of the IEEE International Conference on Computer & Information Science, pages 598-603, 2012. [12] Timothy Wood, Prashant Shenoy, Arun Venkataramani, Mazin Yousif, "Sandpiper: Black-box and gray-box resource management for virtual machines," Proceedings of the Computer Networks, Vol. 53, pages 2923-2938, 2009. [13] Mayank Mishra, Anirudha Sahoo, “On Theory of VM Placement:Anomalies in Existing Methodologies and Their Mitigation Using a Novel Vector Based Approach,” Proceedings of the IEEE International Conference on Cloud Computing, pages 275-282, 2011. [14] Shyam Kumar Doddavula, Mudit Kaushik, Akansha Jain, “Implementation of a Fast Vector Packing Algorithm and its application for server consolidation,” Proceedings of the IEEE International Conference on Cloud Computing Technology and Science, pages 332-339, 2011. [15] Zhuzhong Qian, Ruiqing Chi, Bolei Zhang, Sanglu Lu, "Balancing Resource Utilization for Continuous Virtual Machine Requests in Clouds," Proceedings of the IEEE International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, pages 266-273, 2012. [16] Yasuhiro Ajiro, Atsuhiro Tanaka, “Improving packing algorithms for server consolidation,” Proceedings of the Computer Measurement Group Conference, pages 1-9, 2007. [17] Yufan Ho, Pangfeng Liu, Jan-Jan Wu, “Server Consolidation Algorithms with Bounded Migration Cost and Performance Guarantees in Cloud Computing,” Proceedings of the IEEE International Conference on Utility and Cloud Computing, pages 154-161, 2011. [18] Cristina Bianca Pop, Ionut Anghel, Tudor Cioara, Ioan Salomie, Iulia Vartic, "A Swarm-inspired Data Center Consolidation Methodology," Proceedings of the International Conference on Web Intelligence, Mining and Semantics, pages 1-7, 2012. [19] Fabien Hermenier, Xavier Lorca, Jean-Mar Menaud, Gilles Muller, Julia Lawall, "Entropy: a Consolidation Manager for Clusters," Proceedings of the ACM SIGPLAN/SIGOPS International Conference on Virtual execution environments, pages 41-50, 2009. [20] Anton Beloglazov, Rajkumar Buyya, "Adaptive Threshold-Based Approach for Energy-Efficient Consolidation of Virtual Machines in Cloud Data Centers," Proceedings of the International Workshop on Middleware for Grids, Clouds and e-Science, pages 1-6, 2010. [21] C. Reiss, J. Wilkes, and J. L. Hellerstein, “Google cluster-usage traces: format + schema,” Technical Report, Google Inc., 2011. http://code.google.com/p/googleclusterdata/wiki/TraceVersion2en_US
dc.identifier.urihttp://hdl.handle.net/11455/19934-
dc.description.abstract資料中心是雲端運算裡的重要基礎設施,提供雲端運算服務所需要的各種資源。因此如何有效使用資料中心的資源,已成為了一項重要的議題。為了增加資源的使用效率,近年來的資料中心均廣泛的使用虛擬化技術,藉由將多台虛擬機器同時運行在一台實體伺服器上,以提升實體伺服器的資源使用率。 在本篇論文中,我們研究在虛擬化的環境底下,如何有效使用資料中心的資源。我們提出了一個新的方法,藉由虛擬機器的重新搬移,減少伺服器的資源碎片和降低伺服器的使用資源。該方法可適用於多種資源的處理上。在模擬實驗中,我們從資料中心裡選出五千八百台伺服器的使用紀錄來進行實驗。由實驗結果顯示,我們提出的方法能降低資料中心8%的資源碎片,同時降低3%的資源使用總量。zh_TW
dc.description.abstractData center is an essential infrastructure in cloud computing which offers different types of resources for the services of cloud computing. An important issue is how to effectively use resources in the data center. In addition, in order to improve resource utilization, virtualization technique has been widely used in the data centers in recently years. In same time, when many virtual machines are concentrated on a server, the resource utilization of server can be promoted effectively. In this thesis, we investigate how to reduce resource usage with using virtualization technique in data centers. We propose a method which can reduce resource fragments and resource allocations by using VM migrations. Moreover, the method can be used in multiple resource types. In the simulation experiments, we select 5800 servers from the data centers to proceed resource reallocation. The simulation results show our scheme can decrease 8% resource fragments, and 3% resource allocations.en_US
dc.description.tableofcontents誌謝 i 摘要 ii Abstract iii 目次 iv 表目次 v 圖目次 vi 第一章 緒論 1 1.1 簡介 1 1.2 研究動機與目標 3 1.3 研究假設與限制 4 1.4 論文架構 5 第二章 背景知識與相關研究 7 2.1 資源的有效使用 7 2.2 時間上的資源分配 8 第三章 研究方法 11 3.1 伺服器的資源碎片 11 3.2 時間上資源分配的方法 12 3.3 虛擬機器重新搬移的方法 13 3.4 時間複雜度分析 15 3.5 多種資源的考量 17 第四章 數學分析 18 第五章 實驗模擬與結果分析 20 5.1 模擬環境 20 5.2 模擬結果與分析 21 5.2.1 虛擬機器搬移的門檻分數 21 5.2.2 資源的種類數目 23 5.2.3 虛擬機器的搬移次數 26 5.2.4 不同類型的虛擬機器 28 5.2.5 與其他方法比較 29 5.2.6 伺服器負載平衡的改善 32 第六章 結論與未來研究方向 34 6.1 結論 34 6.2 未來研究方向 34 參考文獻 36zh_TW
dc.language.isozh_TWen_US
dc.publisher資訊科學與工程學系所zh_TW
dc.relation.urihttp://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-1308201321394500en_US
dc.subject雲端運算zh_TW
dc.subjectCloud Computingen_US
dc.subject資料中心zh_TW
dc.subject虛擬機器搬移zh_TW
dc.subjectData Centeren_US
dc.subjectVM Migrationen_US
dc.title一個有效減少資料中心基礎資源分配的虛擬機器搬移方法zh_TW
dc.titleAn Effective VM Migration Method to Reduce Resource Usage in Data Centeren_US
dc.typeThesis and Dissertationzh_TW
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