雲端運算近年來依靠網路虛擬化技術讓大眾得以廣泛且方便地使用，網路虛擬化技術可以讓使用者方便佈署自己虛擬機器在資料中心裡擁有硬體的資源需求。雲端運算環境，有三種"即服務(as a service)"概念，包括硬體設施即服務(IaaS)、平台即服務(PaaS)、軟體即服務(SaaS)。本論文中，我們的研究專注於IaaS概念應用。硬體設施供應商(InP)管理自己資料中心裡的資源，透過虛擬化技術硬體供應商可以管理的範疇例如運算能力，儲存空間，網路架構等。資料中心盡管以虛擬網路的方式提供硬體資源解決效能及網路安全的問題，硬體供應商得面對另一下挑戰也就是虛擬網路映射問題，而所謂虛擬網路映射問題即為如何有效率且靈活的將硬體資源分配給虛擬網路。然而在資料中心的虛擬網路映射問題卻是近幾年才被慢慢重視，雖然兩者問題非常的相似，但其中還是有幾點區隔 : 1)實體網路結構不同，以往傳統的虛擬網路映射問題考慮隨機網路的路由問題，而在資料中心實體網路通常為樹狀結構。2)實體網路設施功能不同，傳統研究考慮實體節點只有處理資料功能，但在資料中心的實體網路中實體節點可以包括交換機、伺服器等不同功能的節點。3)傳統虛擬網路映射問題，對於同一個虛擬網路來說只考慮一個虛擬節點放置入一個實體節點中，但在資料中心同一個虛擬網路，我們認為可以多個虛擬節點同時放置入一個實體節點中。
　　在針對於資料中心的樹型結構，我們提出了啟發式映射演算法將隨意結構的虛擬網路轉換為適合樹型結構映射的星型拓樸，並利用量化虛擬網路的方法在演算法中設計準入控制機制，接著利用裝箱問題的傳統演算法FFD及BFD的優點來將虛擬網路做出分群的效果，並在映射入實體網路中達到最小鏈結成本的考量，最後利用最小成本最大流演算法將虛擬鏈結映射入實體鏈節中。我們透過實驗模擬，驗證我們的想法在成本及收益成本比這幾項目標函數中，確實比傳統的裝箱演算法表現來的較好。

In this thesis, we study the concept of IaaS. Ifrastructure Providers (InP) management data center's resources through virtualization technology. InPs manage areas such as power consumption, storage, network architecture. Although the virtual networks provide hardware resources to tackle the performance and network security issues, InPs have to face another challenge that is virtual network embedding problem. The virtual network embedding problem is how efficient and flexible allocated to the virtual network into hardware resources. However, the virtual network embedding problems in the data center is developed only in recent years. We propose a heuristic algorithm for mapping virtual network of random topology into a form of star topology that is suitable tree structure of data center. We design a scheme to quantify virtual networks and admission control when virtual network request is coming. We exploit the advantages of the traditional bin packing problem algorithm FFD and BFD to make the virtual network dividing into several clusters, and mapping into the substrate network to minimize link cost. Finally, we use the min cost max flow algorithm to map the virtual link into physical links. Through simulation experiments we verified our idea in terms of objective function, e.g. cost, revenue-cost ratio. We find our method indeed better than traditional bin packing algorithms to better performance.

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