博碩士論文 995402010 詳細資訊




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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)
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檔案 [Endnote RIS 格式]    [Bibtex 格式]    至系統瀏覽論文 (2020-7-31以後開放)
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
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指導教授 王尉任(Wei-Jen Wang) 審核日期 2017-8-23
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