Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/19934
標題: 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
An Effective VM Migration Method to Reduce Resource Usage in Data Center
作者: 張天釋
Chang, Tien-Shih
關鍵字: 雲端運算;Cloud Computing;資料中心;虛擬機器搬移;Data Center;VM Migration
出版社: 資訊科學與工程學系所
引用: [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/TraceVersion2
摘要: 
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。

Data 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.
URI: http://hdl.handle.net/11455/19934
其他識別: U0005-1308201321394500
Appears in Collections:資訊科學與工程學系所

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