探究網格計算中最後完工期限對於排程的必要性

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研究生：	曾莉雅 Tseng, Li-Ya
論文名稱：	探究網格計算中最後完工期限對於排程的必要性 Essentiality of Deadline for Task Scheduling in Grid Computing
指導教授：	金陽和 Chin, Yeh-Hao
口試委員:
學位類別：	博士 Doctor
系所名稱：	電機資訊學院 - 資訊工程學系 Computer Science
論文出版年：	2010
畢業學年度：	98
語文別：	英文
論文頁數：	108
中文關鍵詞：	排程啟發式法、總加權延遲時間、指派時間、最後完工期限、總完工時間、網格計算
外文關鍵詞：	Scheduling heuristic, Total weighted tardiness, Assignment time, Deadline, Makespan, Grid computing
相關次數：	點閱：3 下載：0
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目前應用在異質計算或網格計算的排程啟發式法，大部分是著重在尋找最小的總完工時間，而忽略降低成本或考量工作的最後完工期限。最近幾年，有學者致力於將最後完工期限因素加入排程啟發式法的研究中。然而他們將最後完工期限視為工作的最後完成期限，他們的研究目標是期望能夠得到可以在最後完工期限內完成的最多工作數量。另外，有其他學者將最後完工期限當作評估公式中的某一項因子，他們的研究目標則是要得到最大的評估值。最後完工期限也被應用在其他領域，例如：專案管理與作業研究，本研究考慮工作的最後完工期限對於總完工時間在網格計算中是否會有影響是值得被確認。另外，在網格計算的環境中電腦的數量和異質性遠比在異質計算環境更為複雜。其他相關的研究指出，異質計算是基於高速頻寬，導致指派時間可能被忽略，因此異質計算的排程啟發式法可能不能完全直接適用在網格計算。
因此，本研究考慮兩種情況：1)在減少成本情況下求得最小化的總完工時間;及2)在最後完工期限下求得最小化的總完工時間。本研究的模擬實驗將會分為兩個部分：1) Apparent Tardiness Cost Setups-Minimum Completion Time (ATCS-MCT)排程啟發式法是考慮最小化的總完工時間和成本；及2)利用任務的最後完工期限及任務的指派時間驗證本研究所提出的修改後的排程啟發式法可以得到比原始的排程式啟發式法較小的最小化的總完工時間。並且，從實驗結果可以驗証出每個工作的最後完工期限對於總完工時間在網格計算是有好的影響。

Many scheduling heuristics have been dedicated to either Heterogeneous Computing (HC) or Grid computing for the purpose of minimizing makespan, instead of the reduction of cost or deadline of a job. In the last few years, several researches have devoted to the study of scheduling heuristics within deadline factor. However, they treated deadline as a constraint that a task is completed by its deadline, and the maximum total number of completed tasks meeting deadlines is the main objective of these researches. Otherwise, some researches dealt deadline with a weight that the maximum evaluation value is the motive of them. Deadline of a task usually be utilized by other field sciences, such as Project Management and Operations Management, this study considers whether the utilization of deadline affects makespan in Grid computing systems is advantageous should be confirmed. Additional, both the number and the heterogeneities of computers found in Grid computing are more complex than in HC. The relevant HC researches are based on high-speed bandwidth and the assignment time of delivering data has usually been ignored, so the relevant heuristics of HC may not proper for Grid computing reasonably.
Therefore, in response to the appeal to two conditions: 1) minimum makespan under reduce cost; and 2) minimum makespan under deadline. This study simulates two parts: 1) the Apparent Tardiness Cost Setups-Minimum Completion Time (ATCS-MCT) scheduling heuristic considers both makespan and cost; and 2) utilize task deadline and task assignment time to testify that makespans of the proposed scheduling heuristics are better than original scheduling heuristics. Furthermore, it can be verified that utilizing the deadline of each task is really advantageous in a dynamic Grid computing environment.

Contents
Chapter 1    Introduction    1
1    Overview of Grid Computing    1
2    NP-Complete    2
3    Reduce Cost    3
4    Assignment Time and Deadline    4
4.1    Assignment Time    4
4.2    Deadline    5
5    Research Motivation    5
5.1    A Multiple-Factors-based Task Scheduling    6
5.2    A Deadline-based Task Scheduling    6
6    Organization of the Proposal    6
Chapter 2    Previous Works    7
1    Related works    7
2    Overview of Scheduling Heuristics    8
2.1    Static Scheduling Heuristics    8
2.2    Dynamic Scheduling Heuristics    9
3    Heuristics    9
3.1    OLB (Opportunistic Load Balancing)    11
3.2    MET (Minimum Execution Time)    13
3.3    MCT (Minimum Completion Time)    14
3.4    Min-min (Minimum-minimum completion time)    15
3.5    Max-min (Maximum-minimum completion time)    17
3.6    ATCS (Apparent Tardiness Cost Setups)    19
Chapter 3    A Multiple-Factors-based Task Scheduling    21
1    ATCS-MCT    21
2    Static Environment    24
3    Dynamic Environment    25
Chapter 4    A Deadline-based Task Scheduling    26
1    The Motivation    26
2    Framework    27
3    The Proposed Heuristics    29
3.1    OLBII    29
3.2    MCTII    31
3.3    Min-minII    33
3.4    Max-minII    35
4    Simple Examples    37
4.1    OLB and OLBII    38
4.2    MCT and MCTII    41
4.3    Min-min and Min-minII    44
4.4    Max-min and Max-minII    48
Chapter 5    Simulation Model    52
1    Network Simulator    52
2    Constraint    52
2.1    Static Environment    52
2.2    Dynamic Environment    53
3    A Multiple-Factors-based Task Scheduling Model    54
3.1    Scenarios design    54
3.2    Sample for Static    55
3.3    Sample for Dynamic    55
4    A Deadline-based Task Scheduling Model    55
4.1    General    56
4.1.1    Scenarios design    56
4.1.2    Sample for simulation    57
4.2    Analyze MCT vs. MCTII    57
4.2.1    Scenarios design    57
4.2.2    Sample for simulation    59
Chapter 6    Performance Evaluation    60
1    Results for a Multiple-Factors-based Task Scheduling    60
1.1    Static Environment    60
1.1.1    Execution Dispatching Time    61
1.1.2    Completion Time    62
1.1.3    Total Weighted Tardiness    65
1.1.4    Computing Time    67
1.2    Dynamic Environment    70
1.2.1    Execution Dispatching Time    70
1.2.2    Completion Time    71
1.2.3    Total Weighted Tardiness    74
1.2.4    Computing Time    76
2    Results for A Deadline-based Task Scheduling    78
2.1    General    78
2.1.1    Execution Dispatching Time    79
2.1.2    Completion Time    80
2.2    Analyze MCT vs. MCTII    83
2.2.1    Heterogeneities of Tasks and Computers are Different    83
2.2.1.1    Execution Dispatching Time    83
2.2.1.2    Completion Time    85
2.2.2    Heterogeneities of Tasks and Computers are the Same    87
2.2.2.1    Execution Dispatching Time    87
2.2.2.2    Completion Time    88
Chapter 7    Discussions and Conclusions    90
1    A Multiple-Factors-based Task Scheduling    90
1.1    Static Environment    90
1.2    Dynamic Environment    93
2    A Deadline-based Task Scheduling    94
2.1    General    95
2.2    Analyze MCT vs. MCTII    96
References    102

                                

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