近年來雲端系統應用越來越成熟，相對的雲端系統應用也變得更廣泛。MapReduce是一種分散計算系統，雲端系統中越來越多人使用。Hadoop是從Google雲端系統演化出來的開放系統，在雲端的環境中透過虛擬機器或實體機器作分配任務、獲取資源和提供服務，藉由網路將這些功能連結串連起來就可以獲取更大量運算資源，因此軟體運算、硬體效能和網路傳輸速率，都會對分散式運算系統的運算效率產生影響。在許多的論文中提出了一些解決方法，有效的管理伺服器，排程工作依照任務資料運算形式選擇伺服器的選擇等等。為了獲得最大的運算資源，通常會同時開啟多台機器加入運算過程，雖然可以讓運算速度加快，但相對的也產生耗能的問題，因為每台伺服器性能都會有所不同，所以運算效率也不同，當分配到一樣的工作量時有些性能好的伺服器會提前結束工作，閒置在那邊等待其他伺服器完成，這些等待的時間就造成不必要能量浪費。雲端應用Green Mater是基於Hadoop MapReduce做出改進，篩選虛擬機器效能，在不嚴重影響整體系統效能下將虛擬機器效能低的關閉不使用，進而達到節能的效果。這篇論文針對Green Master加入網路傳輸資料速度新的變數，將網路頻寬與虛擬機器的系統效能一起列入篩選範圍，挑選出合適的虛擬機器。在MapRduce過程中，當資料被分散後並分配到指定的虛擬機器上做運算時，這些運算虛擬機器是分散在不同地方，須透過網路傳輸將這些分散資料傳送到指定虛擬機，擁有較好的運算能力的虛擬機器但網路傳輸慢，卻必須浪費等待資料的傳輸時間，所以網路頻寬的快慢勢必也會影響到整體運算完成的時間，提出了Dynamic Green Master改善Green Master網路傳輸的部分。

Hadoop evolves from Google cloud system, and is open system. In cloud circumstance Hadoop allocates the tasks by virtual machines or hardware-based computer, obtains the information, and provides service. Hadoop integrates these above-mentioned functions into Internet to gain a lot of computing resource, therefore, computing, hardware performance, and network transfer rate have effect on process efficiency of distributed system. More papers purpose solution to optimize the Hadoop. For example, efficiency server management and according to the process types of data to schedule the tasks, etc. In order to optimize the process speed, Hadoop can start more computers to deal with tasks, but the strategy result in consuming resource overly. Because each server has different performance, their Operational efficiency is also different. If system allocate the tasks of the same workload to each server, the server having great performance can complete the task rapidly, and these servers idle their time to wait other server to complete their tasks, therefore, the waiting time result in wasting unnecessary performance.
Green Master is based on improvement of MapReduce. Green Master can filter the performance of virtual machines, and not influence system performance to turn off the virtual machines of bad performance. The purpose of Green Master can achieve the goal of energy conservation. 
This paper is aimed at Green Master to add reference value of Network data transfer rate, and filter the proper virtual machines to increase the performance of cloud system once again.
In procedure of MapReduce data is distributed to assigned virtual machines to be processed. Because cloud system must distribute the data through Network transmission, the virtual machines have great performance, but system transfer data to them slowly in Network. The aforementioned condition idle more time to wait the transfer time so Network bandwidth must influence system performance.We propose Dynamic Green Master to improve the Network transfer of Green Master.

目錄
第一章	緒論	1
1.1	研究動機	3
1.2	論文架構	4
第二章	相關研究	6
2.1	Virtual Machine	6
2.2	GFS (Google File System)	9
2.3	MapReduce	10
2.4	Hadoop	14
2.5	Hadoop MapReduce	15
2.5.1	HDFS(Hadoop distributed File System)16
2.5.2	NameNode	18
2.5.3	DataNode	18
2.5.4	Job Tacker	18
2.5.5	TaskNode	18
2.6 Green Master	19
2.6.1 Master	19
2.6.2Green Master (GM)	20
2.6.3 Input File Index	22
2.6.4 Queue	22
2.6.5 Server Information & Benchmark	22
2.6.6 Record	23
2.6.7 Load Balance Optimization	23
2.6.8 Power Saving Algorithm(PSA)	24
2.6.9 Decision	25
2.7	 Benchmark	26
2.8  相關論文研究	26
第三章	Dynamic Green Master	30
3.1Dynamic Green Master System	31
3.1.1 Input File Index	32
3.1.2 Queue	32
3.1.3Server Information	32
3.1.4 VM Bandwidth Score	33
3.1.5 New Benchmark Score	34
3.1.6 Sort New Benchmark Score	34
3.1.7 Load Balance Optimization	35
3.1.8 Power Saving Algorithm	35
3.1.9 Decision	35
3.2系統流程圖	36
第四章	實驗結果以及效能分析	38
4.1.	實驗環境	38
4.2.	Green Master 網路傳輸的影響	40
4.3.	Dynamic Green Master Simulation Result	42
第五章	結論以及未來展望	48
參考文獻 (References)	49
圖目錄
圖2.1、寄宿虛擬化實現方式	7
圖2.2、原生虛擬化實現方式	8
圖2.3、MapReduce簡易結構圖	13
圖2.4、Key/Value示意圖	13
圖2.5、Hadoop環境建置	16
圖2.6、Green Master架構圖	21
圖3.1、Dynamic Green Master Architecture	31
圖3.2、系統流程圖	36
圖4.1、虛擬機器Benchmark Score和網路頻寬	39
圖4.2、Green Master在不同網路頻寬系統時間	40
圖4.3、Green Master在不同網路頻寬和資料量系統時間	41
圖4.4、GM與DGM系統時間	43
圖4.5、GM與DGM單位時間耗能	44
圖4.6、GM與DGM的系統耗能	45
圖4.7、GM、DGM以及網路頻寬優系統耗能	46
圖4.8、耗能比較圖	47
 
表目錄
表2.4 Google MapReduce與Hadoop MapReduce比較圖	14
表3.1 ε實驗環境表	14
表4.1 Green Master BS排序	42
表4.3 兩台VM在低頻寬實驗環境	47

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