隨著網格運算的盛行，有越來越多的研究針對如何分配網格資源給工作流程中的每個任務提出不同的解決方法。有許多因素會影響到分配的結果，其中之一便是工作流程是屬於計算密集型或是資料密集型，許多研究會針對其中一種環境來進行。在本篇論文中，我們將根據先前所提出的運用機率架構之動態資源規劃並加進資料傳輸的影響因素。我們的目標是希望能夠動態地分配資源給工作流程中的任務，進而使整個工作流程能夠在使用者所期望的時間內完成的機率最大化。我們提出兩種不同的演算法來處理網格環境下的動態資源規劃，包括largest deadline completion probability (LDCP) and smallest deadline completion probability (SDCP)。此外，為了改善資料傳輸的問題，我們提出了push-based的規劃演算法與先前研究中的pull-based與workflow-based的方法進行比較。我們將使用GridSim網格模擬器來建立網格環境，評估不同演算法之間的效能差異。

Recent enthusiasm in grid computing has resulted in a tremendous amount of research in resource scheduling techniques for tasks in a (scientific) workflow. There are many factors that may affect the scheduling results, one of which is whether the application is computing-intensive or data-intensive. Most of the grid scheduling researches focus on a single aspect of the environments. In this thesis, we base on our previous work, a probability-based framework for dynamic resource scheduling, and consider data transmission overhead in our scheduling algorithms. 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 two algorithms for the dynamic resource scheduling in grid environment, namely largest deadline completion probability (LDCP) and smallest deadline completion probability (SDCP). Furthermore, considering the data transmission overhead, we propose a suite of push-based scheduling algorithms, which schedule all the immediate descendant tasks when a task is completed. These are algorithms will be compared to the pull-demand scheduling algorithms in our previous work and workflow-based algorithms proposed by other researchers. We use GridSim toolkit to model the grid environment and evaluate the performance of the various scheduling algorithms.

List of Content
CHAPTER 1 - Introduction 1
1.1. Background 1
1.2. Motivation 1
CHAPTER 2 - Literature Review 3
2.1. Grid architecture 3
2.2. General grid environment and scheduling 4
2.2.1. Static resource scheduling 4
2.2.2. Dynamic Scheduling 6
2.3. Stochastic scheduling 7
2.3.1 Minimize expected flowtime 7
2.3.2 Minimize expected makespan 8
2.3.3 Others 8
2.3.4 Comparison 8
2.4. Issue in data dependency 10
CHAPTER 3 - Problem Description 11
3.1. System architecture 11
3.2. Problem Description 13
CHAPTER 4 - Scheduling Algorithms 15
4.1 Autonomy of local site 15
4.2. Dynamic Scheduling Algorithm 17
4.2.1 When to schedule a task 17
4.2.2 Scheduling policy 17
CHAPTER 5 - Performance Evaluation 22
5.1 Simulation Environment Settings 24
5.1.1. Computing power 25
5.1.2. Data dependency 25
5.1.3. Local workload 28
5.2 Experimental Result 28
5.2.1. Data Dependency 28
5.2.2. Resource Computing Power 34
5.2.3. Resources local load 38
5.2.4. Complicated Situation 40
CHAPTER 6 - Conclusion 44
References 45

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