臺灣博碩士論文加值系統

近年來，雲端運算已成為新興的網路議題，其中IaaS (Infrastructure as a Service)的服務也日漸盛行。在IaaS中，雲端資源管理系統負責管理資源的配置，而不同的資源排程及配置會導致實際的電能消耗、資源使用率及實體機器負載的不同，因此，如何設計雲端資源管理系統是一個重要的議題。在過去的研究中，許多學者已提出各種雲端資源配置法，來提昇雲端系統的效能，節省能源或提高使用者服務品質。然而，雲端服務需兼顧系統廠商與顧客的滿意度，因此，如何讓雲端資源管理能提供如此的彈性就成為一個有趣的議題。在本篇論文中，我們提出了一個雲端服務的MaxFit配置演算法，使系統管理者可以在進行資源的配置及排程時兼顧使用者滿意度及節能，並進行動態搬移來增進系統效能。我們針對不同的使用者滿意度及節能偏好來檢測所提出的方法，結果顯示與偏重使用者滿意度及偏重節能的資源配置法相比較，MaxFit可根據系統偏好達到彈性配置及調整，並可同時考量使用者滿意度及節能。

In recent years, cloud computing has become a popular research subject. In the IaaS (Infrastructure as a Service), the cloud resource management system is responsible for managing and allocating resources and different resource scheduling and allocation scheme will lead to different energy consumption, resource utilization and user satisfaction. Thus, how to design an efficient resource management system is an important issue. In the past studies, many researchers have proposed a variety of cloud resource allocation schemes to improve the performance, energy saving or user satisfaction of a cloud system. However, cloud services need to consider both vendor and user satisfaction, and thus how to manage the cloud resource to provide such flexibility has become an interesting issue. In this thesis, we propose a cloud resource allocation algorithm called MaxFit to allow a system administrator to manage resources taking into account user satisfaction and energy-saving. Simulation results show that, along with on-line Virtual Machine migration, comparing with the other algorithm that either emphasis on user satisfaction or energy-saving, MaxFit achieves better flexibility to meet administrators’ preferences, and performs well in various system parameter settings.

目錄
1 導論 1
1.1 雲端的基礎特徵 1
1.2 雲端的部署模型 1
1.3 雲端的服務模式 2
1.4 雲端資源管理 3
1.5 研究動機與目的 3
1.6 論文貢獻 4
1.7 章節概要 4
2 文獻探討 5
3 高綠能雲端資源管理系統 7
3.1 雲端環境 7
3.2 高綠能雲端資源管理系統 8
3.3 配置演算法分析與探討 12
4 模擬 15
4.1 模擬環境與參數 15
4.2 模擬分析 16
4.3 模擬結果 16
5 結論與未來展望 23
參考文獻 24
圖目錄
圖 1–1雲端的部署模型 2
圖 1–2雲端的服務模式 3
圖 3–1雲端環境 7
圖 3–2動態搬移 9
圖 3–3動態搬移流程 10
圖 3–4系統流程 10
圖 3–5 MaxFit allocation process 14
圖 4–1平均U值 (α=0.1 β=0.9) 17
圖 4–2平均U值 (α=0.3 β=0.7) 17
圖 4–3平均U值 (α=0.5 β=0.5) 17
圖 4–4平均U值 (α=0.7 β=0.3) 18
圖 4–5平均U值 (α=0.9 β=0.1) 18
圖 4–6平均每小時VM平均耗能 18
圖 4–7整體耗能 19
圖 4–8動態搬移-平均U值 (α=0.1 β=0.9) 20
圖 4–9動態搬移-平均U值 (α=0.3 β=0.7) 20
圖 4–10動態搬移-平均U值 (α=0.5 β=0.5) 20
圖 4–11動態搬移-平均U值 (α=0.7 β=0.3) 21
圖 4–12動態搬移-平均U值 (α=0.9 β=0.1) 21
圖 4–13動態搬移-平均每小時VM平均耗能 21
圖 4–14動態搬移-整體耗能 22
圖 4–15 搬移次數 22
表目錄
表 3–1使用者檔案 8
表 3–2 MAXFIT配置範例 11
表 3–3 FIRSTFIT配置範例 13
表 4–1模擬實體電腦規格數量 15
表 4–2 VM規格 15
表 4–3使用者需求 15
表 4–4 搬移次數 22

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