近年來由於網格運算的盛行，有越來越多的研究針對如何分配分散的網格資源給工作流程中的每個任務提出不同的解決方法，而過去大部分的研究主要是著重在如何降低完成一個工作流程所花費的時間，而且他們大多是將工作流程中的每個任務在不同的資源執行所需的時間當成是固定的常數來處理，因此在本篇論文，我們提出一個以機率為架構的網格資源分配模型，將不同的任務在不同的資源執行所需要花費的時間當成是一個常態分佈，因此每個任務在相對應的資源所要花費的執行時間變成了一個變數而不是常數，因此將一些不確定性納入考量，我們的目標是希望能夠動態的分配資源給工作流程中的任務，進而使整個工作流程能在使用者所期望的時間內完成的機率最大化，也就是找出一個能符合使用者所希望完成的時間內完成工作流程的資源分配，我們提出了三個演算法來動態的處理網格資源分配，包括integer linear programming, the max-max heuristic and the min-max heuristics，並也提出兩個將執行時間當成常數的方法做比較，為了提高實驗的可靠性，我們採用真實的一個工作流程應用來進行實驗，並以模擬的方式進行各種不同資源的環境，最後我們得到的結果顯示在大部份的形況下，the min-max heuristics 表現的比其他方法好。

Recent enthusiasm in grid computing has resulted in a tremendous amount of research in resource scheduling techniques for tasks in a workflow. Most of the work on resource scheduling is aimed at minimizing the total response time for the entire workflow and treats the estimated response time of a task running on a local resource as a constant. However in a dynamic environment such grid computing, the behavior of resources simply cannot be ensured. In this thesis, thus, we propose a probabilistic framework for resource scheduling in a grid environment that views the task response time as a probability distribution to take into consideration the uncertain factors. The goal is to dynamically assign resources to tasks so as to maximize the probability of completing the entire workflow within a desired total response time. We propose three algorithms for the dynamic resource scheduling in grid environment, namely the integer programming, the max-max heuristic and the min-max heuristic. Experimental results using synthetic data derived from a real protein annotation workflow application demonstrate that the proposed probability-based scheduling strategies have similar performance in an environment with homogeneous resources and perform better in an environment with heterogeneous resources, when compared with the existing methods that consider the response time as a constant. Of the two proposed heuristics, min-max generally yields better performance.

CHAPTER 1 - Introduction 1
CHAPTER 2 - Literature Review 4
2.1 Clustering computing to grid environment 4
2.2 General grid environment 5
2.2.1 Static resource scheduling 6
2.2.2 Dynamic resource scheduling 9
2.3 Comparison 10
CHAPTER 3 - The System Model 14
3.1 System architecture 14
3.2 Autonomy of grid resources 16
CHAPTER 4 - Dynamic Scheduling Algorithms 18
4.1 Method to specify individual threshold 18
4.2 Scheduling algorithm 20
4.2.2 Integer linear programming (ILP) scheduler 21
4.2.2 Max-max and min-max schedulers 22
CHAPTER 5 - Performance Evaluation 27
5.1 Simulating Environment Settings 28
5.2 Experimental Results 30
5.2.1. Success rates in the homogeneous environment 31
5.2.2 Success rates in the heterogeneous environment 32
5.2.3. Scheduling Strategies Comparison 34
5.2.3.1 min-min v.s. max-min 34
5.2.3.2 min-max v.s max-max 36
5.2.3.3 min-max v.s. min-min 44
CHAPTER 6 - Conclusions 48
References 49

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