由於雲端運算提供運算資源隨用隨租、用多少即付多少的特性，發展一個能夠動態根據服務需求來調整運算資源的方法已成為關鍵議題。本文提出一個基於監控視訊影像分析結果來動態調整雲端運算資源的方法，在此方法中監控視訊內容被切割成許多片段並分配給多個運算節點同時進行分析，透過所提出的內容感知工作量估算方法針對系統工作量進行預測，在不影響服務品質的情況下動態調整運算節點數量，盡可能地將使用的運算資源降到最低。實驗結果顯示本論文所提出的方法可以有效地預測系統工作量，在不影響服務品質的情況下，在運算成本及工作完成率上的效能表現較其他方法優異。

As cloud computing platform provides computing power as utilities, it is important to de velop a mechanism to adaptively adjust the resources needed for handling cloud service. In this paper, a computing resource minimization framework for cloud-based surveillance video analysis systems is proposed. Video streams are divided into clips and then multiple processing nodes are used to handle the clips. While the quality-of-service (QoS) is maintained, the proposed framework dynamically adjusts the number of processing nodes based on a proposed content-aware workload estimation mechanism. Experimental results show that the proposed mechanism successfully predicts the variability of system workload while QoS is maintained and outperforms other mechanisms in terms of virtual machine (VM) cost and job finished ratio.