長久以來，排程一直是一個重要的研究議題。基本上，排程演算法就是決定工作的執行順序，並且被廣泛的應用在不同領域，包括工業、商業、資訊科技和我們的日常生活。一個好的排程可以有效的增進系統效能，相反的，不好的排程不只是降低系統效能，更有可能導致成本增加。各式各樣的工作在不同環境下會有不同的目標與限制，為了達到較高的效率，針對不同的問題與應用，排程演算法應該要適度的調整以符合其需求。這篇論文對於一些在異質環境的應用提出了幾個對應的排程演算法，包括即時系統、嵌入式系統與雲端計算。
首先，這篇論文將即時系統的概念加到了手持嵌入式系統中，並且提出了一個易於實作的排程演算法同時考量工作期限與節能。對於即時多核心系統，本篇論文也提出了一個新的排程演算法以達到負載平衡與節能的目標。這個演算法稱為功率與期限感知多核心排程演算法，它同時考量多個負載的評斷標準，其中也包含了工作期限與一個新提出的考量因子。除了嵌入式系統外，這篇論文也為雲端計算提出了兩個方法去處理虛擬機排程問題。一個是包含粒子群聚的蟻群最佳化演算法，另一個則是包含遷移與粒子群聚的蟻群最佳化演算法。其中前者結合了螞蟻演算法和粒子群聚最佳化，並透過歷史資訊去預測新進工作的負載。此外，包含遷移與粒子群聚的蟻群最佳化演算法則是包含粒子群聚的蟻群最佳化演算法的進階版本，它新增了虛擬機遷移機制以達到更高的資源使用率並使系統可以服務更多的使用者。

Scheduling has obtained its name to be an important research issue for years. Basically, it is used to determine the sequence of jobs and has been widely applied in different domains, including industry, business, technology and our daily life. A good scheduling can significantly improve the performance of a system. On the contrast, an inferior scheduling not only decreases the system performance, but also increases the cost.
Various jobs involve all kinds of objectives and limitations in different environments. In order to get high performance, scheduling algorithm must be adaptive to the demand of a variety of problems and applications. This thesis proposes several scheduling algorithms for the applications in heterogeneous environments, including real-time system, embedded system and cloud computing.
First, this thesis adds the concept of real-time system to portable embedded systems, and proposes a scheduling algorithm that considered task deadline and power-saving at the same time, and which can be easily implemented on an actual hardware device. Second, a novel scheduling algorithm is proposed for real-time multi-core systems to balance the computation loads and save power. The developed algorithm, called power and deadline-aware multi-core scheduling (PDAMS), considers multi criteria including a novel factor, and task deadline simultaneously.
Except for embedded system, this thesis also presents two methods to deal with the virtual machine (VM) scheduling problems in a cloud environment. One is ant colony optimization with particle swarm (ACOPS) and the other is ACOPS with migration (ACOPSM). The former combines ant colony optimization (ACO) and particle swarm optimization (PSO) and uses historical information to predict the workload of new input requests. ACOPSM is an enhanced version of ACOPS. It adds a VM migration mechanism to ACOPS to achieve higher memory utilization and enables the system to serve more requests.

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