雲端運算(Cloud Computing)為近年來新興的一項概念，透過網際網路，可提供使用者豐富的應用程式與虛擬化資源，如儲存空間、CPU、記憶體等。相較於傳統資料中心，雲端資料中心可減少龐大的管理成本，而虛擬機器在雲端資料中心之服務佔有重要角色。由於雲端資料中心可能分佈於許多地區，而每個地區的資料中心其網路狀態不盡相同， 因此，虛擬機器佈建的位置將影響使用者存取的效能。

本研究使用Eucalyptus建置一雲端資料中心，並提出Network Utilization Constrained演算法，其係基於目前使用者與節點控制器間之網路使用率決定虛擬機器佈建的位置，並透過OpenFlow Switch管理使用者與虛擬機器間的流量。

本研究針對節點控制器位於同一網域與不同網域進行分析，包含兩種情境，一種為正常情況下，另一種為節點控制器發生網路瓶頸的情況，根據結果顯示，於相同網域中，採用本研究提出之演算法所建置之虛擬機器其檔案傳輸時間與Eucalyptus內建之Round Robin與Greedy演算法比較，可提升2.63%與68.72%，若發生網路瓶頸，則可提升32.79%與60.24%。而於不同網域中，可提升21.99%與52.10%，發生網路瓶頸時，可提升22.74%與49.86%。

Cloud computing emerges as a novel application service layers. It provides diversity of applications and virtualization resources, such as storage services, CPU, and memory. Thus, when it is compared with the traditional data center, cloud data center becomes very attractive solution for enterprises where enormous management cost could be reduced. Virtual machines hold important role for cloud data center service. Since the data center may be distributed over many regions, and the network conditions within cloud data center is different, so the deployment of virtual machines will influence service performance obtained by users.

This study implements Eucalyptus cloud computing platform as a cloud data center system, and proposes network utilization constrained scheduling algorithm that works according to the current network status between users and associated node controllers for deploying the services. In addition by OpenFlow switch is implemented to manage the intended flow within users and virtual machines.

The experiment is conducted upon node controller in the same and different subnet. For each experiment two scenarios are performed, First scenario is conducted within normal network condition, second scenario is considering bottleneck event at node controller. The experiment results indicate that in the same subnet, the virtual machines deployed with network utilization constrained scheduling algorithm can improve the transmission time 2.63% and 68.72% compare to traditional Round Robin and Greedy algorithms consecutively. In considering bottleneck event, the proposed method can improve 32.79% and 60.24%. In the different subnet scenario, the proposed mechanism improves 21.99% and 52.10%, eventually with bottleneck event the system is improved as 22.74% and 49.86%.

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