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Author: 范珮琪
Pei-chi Fan
Thesis Title: 為雲端資料中心所設計之高效率虛擬機器調控機制
Efficient Virtual Machine Provision Mechanism for Cloud Data Centers
Advisor: 羅乃維
Nai-wei Lo
Committee: 賴源正
Yuan-cheng Lai
Kuo-hui Yeh
Degree: 碩士
Department: 管理學院 - 資訊管理系
Department of Information Management
Thesis Publication Year: 2013
Graduation Academic Year: 101
Language: 英文
Pages: 61
Keywords (in Chinese): 雲端運算基礎設施即服務能源消耗資源管理虛擬技術整合技術遷移技術服務層級協議
Keywords (in other languages): Cloud Computing, IaaS, Energy Consumption, Resource Management, Virtualization, Consolidation, Migration, Service Level Agreement (SLA)
Reference times: Clicks: 437Downloads: 6
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雲端運算(Cloud Computing)最大優勢為其具有實體主機虛擬化(virtualization)與虛擬主機遷移技術(migration):運行在使用過低實體主機的虛擬主機能夠透過遷移技術遷移至其他主機,實體主機歸功於虛擬化技術能使多台虛擬主機運行於其上,透過隔離技術安全共享其所提供之資源,並將使用過低或者閒置的主機切換至低耗能模式以達成資源有效運用以及節省不必要的能源消耗之目的,此兩技術總稱為整合技術(consolidation)。

Network technology is more and more maturity and the diversity of the computing devices used to connect to Cloud environments, such as computers, laptops, smartphones, TVs and cameras… and so on. In this condition, the Cloud industry boomed in recent years. Cloud data centers can be regarded as the heart of the entire Cloud computing, it provides computing, processing and storage services. When the users are more, the energy consumption will be higher. Therefore, how to avoid unnecessary waste of resources which leading to high energy consumption is the problem that we need to concern.
The greatest advantages of the Cloud Computing are the virtualization from the physical host and the virtual machine migration technology: Using the migration technology, a VM (i.e. virtual machine) which running through the underutilization host moves to the other one. Attributing to virtualization technology, a host enables to run multiple VMs residing on it and VMs share resources by isolation technology. To achieve the purpose of effective using resources and saving unnecessary energy consumption by turning the underutilization host to the low-power mode. Combing with this two techniques are referred to as consolidation technology.
In this thesis, we proposed the efficient virtual machine provision mechanism for Cloud data centers: it is a kind of heuristic algorithm. Besides, using Cloud resources efficiently and caring about the trade-off between the energy consumption and quality of services (QoS). In addition to present the multi-faceted evaluation criteria and comparing with the algorithm which using statistical forecasting method based on using the historical information (MIPS) of the VMs, called LrMmt. From the experiment results showed that our proposed method not only using resources more efficiently, but also less number of VM migrations and less number of SLA violations in the Cloud data centers.
In addition, we also consider another scenario: giving the priority to the VMs in order to considering the importance of the applications running on the VM. Choosing a lowest priority of VM on a host and minimum migration time of VM by the new VM selection policy. We used three cases for assign different migration overhead and compared with LrMmt. The experiment results shown that whether considering priority or not, our consolidation policy are better than LrMmt.

中文摘要 Abstract 致謝 Contents List of Figures and Tables Chapter 1 Introduction Chapter 2 Related Work 2.1 Factors of Affecting Power Consumption 2.1.1 One-Dimensional: CPU 2.1.2 One-Dimensional: Memory 2.1.3 Multi-Dimensional: CPU, Memory, Disk and other else 2.2 Power Saving Considerations 2.2.1 Trade-Off Between Migration and SLA Violation 2.2.2 Only Considering Migration 2.3 The statistical method - LrMmt Chapter 3 Efficient Provision Mechanism for Cloud Data Centers Resources 3.1 System Model 3.1.1 Power Model 3.1.2 Migration Cost Model 3.1.3 SLA Violation Model 3.2 The Virtual Machine Consolidated Policy 3.2.1 Overloading Detection Scheme 3.2.2 VM Selection Scheme 3.2.3 VM Placement Scheme 3.3 Considering Priority of Virtual Machines Chapter 4 Simulation and Results 4.1 Experiment Setup 4.2 Performance Metrics 4.3 Workload Data 4.4 Simulation Results and Analysis 4.4.1 Optimization of Resource Consolidate Policy 4.4.2 All VMs with the Same Priority 4.4.3 Considering different priorities of VMs Chapter 5 Conclusion References

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