並不存在任何一個啟發式演算法或是傳統演算法能夠找出所有雲端排程問題的最佳解。為了充份利用這些啟發式演算法強處，超啟發式演算法被提出來分析每一個底層啟發式演算法的特性，並且決定底層全域 (或是區域) 搜尋啟發式演算法的使用時機點。這種高層策略所主導的互動機制能促使超啟發式演算法更容易解決不同種類的最佳化問題。本篇論文將提出「以超啟發式為基礎之工作流排程演算法」，它使用兩種偵測運算子 (分別是多樣性偵測運算子，以及解提升偵測運算子) 動態地決定切換底層啟發式演算法時間點。當在切換底層啟發式演算法時，也會將前一個底層演算法所獲得的解回傳至高層管理中心。接著，高層擾動策略將使用演化資訊來微調回傳解後，再將微調後的解傳給下一個新選定底層演算法。為了進一步衡量所提方法，本篇論文將使用 cloudsim 模擬軟體建置雲端模擬環境，來比較所提方法與其它六種排程演算法的效果。根據數據結果顯示與分析，本篇論文所提出演算法是非常有潛力。

None of the metaheuristics and the traditional algortihms ever proposed are perfect for all the cloud scheduling problems. In order to leverage the strengths of all the metaheuristics, the hyper-heuristic was proposed to analyze the features of the low-level metaheuristics
and to decide the timing of using a particular low-level metaheuristic to do the global or local search. The interaction mechanism of high level strategies makes it easier for applying the hyper-heuristic to optimization problems. In this thesis, we propose a “hyper-heuristic-based workflow scheduling algorithm,” which uses two detection operators—the diversity detection and improvement detection operators—to dynamically decide when to change the low-level
metaheuristic. At the time of the change, the solution obtained by the old low-level metaheuristic will be returned to the high level control center. Then, the high level perturbation method will use the evolution information to fine-tune the returned solution before passing it on to the new low-level metaheuristic. To evaluate the performance of the proposed method, we compare it with six state-of-the-art scheduling algorithms, by implementing all of them on cloudsim, a cloud simulator. The experimental results show that the proposed method is quite promising.

論文審定書 i
誌謝 iii
摘要 vi
Abstract vii
List of Figures x
List of Tables xi
Chapter 1 簡介 1
1.1 動機 3
1.2 論文貢獻 3
1.3 論文架構 4
Chapter 2 文獻探討 5
2.1 雲端排程特色與現況 5
2.2 工作流排程問題 6
2.3 工作流排程演算法 8
非啟發式排程演算法 9
2.3.1.1 Min-min 排程演算法 9
2.3.1.2 Max-min 排程演算法 10
2.3.1 啟發式排程演算法 11
2.3.2.1 基因演算法 13
2.3.2.2 螞蟻演算法 15
2.3.2.3 粒子群聚演算法 16
2.3.2.4 模擬退火演算法 19
2.4 超啟發式演算法 20
2.4.1 超啟發式選擇策略 21
2.4.2 超啟發式接受策略 22
2.4.3 超啟發式策略使用模擬退火演算法 23
2.4.4 超啟發式演算法擾動機制 25
2.4.5 超啟發式演算法架構 26
2.4.6 小結 26
2.5 總結 28
Chapter 3 研究方法 29
3.1 超啟發排程演算法 29
3.2 多樣性偵測運算子 32
3.3 解提昇偵測運算子 34
3.4 解擾動策略 35
Chapter 4 實驗結果 37
4.1 實驗架構與流程說明 37
4.1.1 執行環境 37
4.1.2 資料集介紹 37
4.1.3 參數設定 39
4.2 解品質與執行時間分析 41
4.3 收斂趨勢分析 45
4.4 總結 48
Chapter 5 結論與未來展望 49
5.1 結論 49
5.2 研究遭遇困難 49
5.3 未來展望 50
Bibliography 52

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