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論文名稱(外文):Dynamic Grouping integrated Neighboring Search Job Allocation Scheduler for Hadoop MapReduce in Heterogeneous Computing Environments
指導教授(外文):Sun-Yuan Hsieh
外文關鍵詞:HadoopHeterogeneous computing environmentsHeterogeneous workloadsMapReduceScheduling
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隨著網路的蓬勃發展、雲端環境的成長、網路資料量爆炸性的遞增,雲端運算成為分散式系統中近幾年來炙手可熱的名詞,MapReduce是雲端運算中一個很重要的架構,而Apache Hadoop則是其中一個實現MapReduce且較廣為人知的雲端運算平台。在大型的數據中心執行任務時,不同的任務往往需要使用到不同的資源,但Hadoop本身預設的排程是採用First-Come-First-Service (FCFS-先到先服務)策略,這可能會造成資源利用度的不平衡。隨著各方學者針對工作排程的研究與改進,從DMR演算法修改成JAS、JASL、甚至DJASL。從同質環境與工作深入考量至異質環境與異質工作,利用工作的分類對應至相應的佇列,而JAS及DJASL在資源利用度方面都有一定成效的提升,且DJASL更針對資料區域性做考量有效降低額外的網路傳輸流量,但美中不足的是DJASL在有效的提升資料區域性的同時對於工作執行效能並未能有明顯的成長。因此,在本文中我們提出了一個新的工作排程方式叫做DGNS,利用集群以及鄰近搜尋的概念,以綜觀的方式同時考量MapReduce(計算)以及HDFS(資料儲存)層面,除了有效地平衡資源利用度的問題。可以有相仿的高資料區域性並且能擁有較好的效能表現。
With the rapid development of the Internet, the growth of the cloud environment, and the amount of explosive increasing network data, cloud computing, which was a new noun in distributed computing systems in recent years become a hot term. MapReduce is one of a very important cloud computing architecture, while the Apache Hadoop is one of the more well-known implement MapReduce and cloud computing platforms. The resources required for jobs executed in a large data center very according to the type of jobs. Gen-erally, there has two kinds of Jobs, CPU-bound jobs and I/O-bound jobs, which demand different resources but run simultaneously in the same cluster. The default job scheduler of Hadoop is first-come-first-served (FCFS) and thus, may cause unbalance resource uti-lization. Given various job workloads, the JAS categorizes jobs and then assigns tasks to a CPU-bound queue or an I/O-bound queue. However, the JAS exhibited a locality problem, which was addressed by developing a modified JAS called the job allocation scheduler with locality (JASL) and create dynamic job allocation scheduler with local-ity (DJASL) which exhibited better performance and reduce extra network traffic flow. But the drawback of (DJASL) is (DJASL) effectively enhance data locality but failed to have significant growth on job execution performance. Therefore, in this paper we proposes a job scheduler with dynamic grouping integrated neighboring search strategy called (DGNS), which designed to balance resource utilization and take performance and data locality improvement into account in heterogeneous computing environments. The DGNS algorithm exhibits more favorable performance and data locality compared with Hadoop, DMR, JAS, and DJASL.
1 Introduction 1
2 Background 7
2.1 Job Workloads 7
2.2 Default Scheduler of Hadoop (FCFS) 8
2.3 The Problem of Hadoop 10
2.4 Dynamic Map Reduce Scheduler (DMR) 11
2.5 Job Allocation Scheduler (JAS) 14
2.6 Job Allocation Scheduler with Locality (JASL) 15
2.7 Related Work 16
3 The Proposed Algorithms 20
3.1 Job Classification 21
3.2 Ratio Table 23
3.2.1 Capability of TaskTracker and Slot Setting 24
3.2.2 Capability of DataNode 26
3.3 Grouping and Allocation 30
3.3.1 Grouping 30
3.3.2 Data Block Allocating 31
3.4 Neighboring Search 31
4 Performance Evaluation 34
4.1 Experiment Environment 34
4.2 Results 36
4.2.1 Individual Performance of Each Workloads 36
4.2.2 DGNS in Heterogeneous Computing Environments 37
4.2.3 Performance and Data Locality of DGNS 39
5 Conclusion 42
Appendices 43
Appendix A 43
A.1 Using VirtualBox to build cluster 43
Appendix B 47
B.1 Hadoop Setup 47
Bibliography 52

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