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研究生:紀秉賢
研究生(外文):Ping-Hsien Chi
論文名稱:開放原始碼 MapReduce 架構下完全獨立擴張樹檢測
論文名稱(外文):Examing the Completely Independent Spanning Tree under the Frame Work of Open Source MapReduce
指導教授:楊進雄楊進雄引用關係
指導教授(外文):Jing-Shyong Yang
學位類別:碩士
校院名稱:國立臺北商業技術學院
系所名稱:資訊與決策科學研究所
學門:電算機學門
學類:電算機應用學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:中文
論文頁數:42
中文關鍵詞:開放原始碼完全獨立擴張樹
外文關鍵詞:MapReduceHadoop
相關次數:
  • 被引用被引用:0
  • 點閱點閱:208
  • 評分評分:
  • 下載下載:51
  • 收藏至我的研究室書目清單書目收藏:1
雲端運算並不是一種新的網路技術而是一種新的網路概念,是藉由網路串聯 不同電腦之間的相互合作,藉由網路的概念來產生相對應的技術,本質來自於分 散式運算與網格運算。分散式運算是將一個工作切分成很多小型工作,再分別配 給多台電腦運算,最後再匯集所有的結果來完成單機的工作。
網格運算則是分散式運算的一種延伸,主要特色是將不同平台、等級架 構的電腦藉由分散式運算來做整合,所以雲端運算與網格都是一種分散式的 延伸。網格運算是強調整合眾多不同的平台,而雲端運算是強調平台,在本 機有限的資源利用網路來取得運算。因此,建置分散式運算的雲端平台研究 有其必要性。本研究建置雲端分散式檔案系統Hadoop,Hadoop Distributed File System(HDFS),使用四台及八台電腦來佈建四台及八台分散式運算環境。分 散式運算是來處理大量的運算,本研究藉由窮舉的方式來測試 Hadoop 分散式檔 案系統。而窮舉在演算中是常被拿來尋找及驗證規則,不過目前窮舉方式都是大 多數以 MPI(Message Passing Interface)來實現,較少在雲端平台上來實現,因 此本研究藉由雲端平台來實現完全獨立擴張樹窮舉演算法驗證。
Cloud computing is not a new network technology, but a new concept of the network. By the network of cooperating the different computers and the concept of the Internet to generate the corresponding technology, essentially, from the dis- tributed computing and grid computing. Distributed computing is a work into many small works, and then were assigned to more than one computer to complete the work of stand-alone, and finally collected all results.
Grid computing is an extension of distributed computing, the main features are different platforms, level-based computer by distributed computing integrate, cloud computing and grid computing are an extension of distributed computing. Grid computing is emphasized the integration of many different platforms, but cloud computing is emphasized the platform, the machines with limited resources use the Internet to complete the computation. Therefore, the research about building distributed computing via cloud platform research is necessary. This paper build cloud distributed file system, Hadoop, Hadoop Distributed File System (HDFS), using four and eight computers to deploy four and eight distributed computing environments. Distributed computing handles a large number of operations, this paper uses exhaustive way to test Hadoop Distributed File System. Exhaustive calculations are often used to find and validation rules, so far, MPI (Message Passing Interface) the way of exhaustive calculation is to use, there are few platforms in the cloud, so this paper uses the cloud platform to achieve exhaustive validation of completely independent spanning tree algorithm.
中文摘要. . . . . . . . . . . . . . . . . i
英文摘要. . . . . . . . . . . . . . . . ii
誌謝. . . . . . . . . . . . . . . . . . iii
目錄. . . . . . . . . . . . . . . . . .. v
表目錄. . . . . . . . . . . . . . . . . vii
圖目錄. . . . . . . . .. . . . . . . . viii
程式列表目錄. . . . . . . . . . . . . . . ix
一、研究背景. . . . . . . . . . . . . . . . 1
二、文獻探討. . . . . . . . . . . . . . . . 3
2.1 雲端運算. . . . . . . . . . . . . . . 3
2.1.1 Microsoft. . . . . . . . . . . . . 8
2.1.2 IBM . .. . . . . . . . . . . . . . 9
2.1.3 Oracle . . . . . . . . . . . . . . 9
2.1.4 Amazon . . . . . . . . . . . . . . 9
2.1.5 Google . . . . . . . . . . . . . .10
2.2 Hadoop . . . . . . . . . . . . . . .12
2.3 HDFS . . . . . . . . . . . . . . . .13
2.4 Mapreduce. . . . . . . . . . . . . .18
2.5 完全獨立擴張樹. . . . . . . . . . . . .23
三、分散式演算法設計概述. . . . . . . . . . .25
3.1 基本架構. . . . . . . . . . . . . . . 25
3.2 演算法設計. . . . . . . . . . . . . . 25
四、實驗結果. . . . . . . . . . . . . . 32
五、結論. . . . . . . . . . . . . . . . .38
參考文獻. . . . . . . . . . . . . . . . 39
附錄A: MapReduce 程式列表. . . . . . . . . 41
[1] Apache. Apache Hadoop. http://hadoop.apache.org.
[2] Apache. MapReduce. http://hadoop.apache.org/docs/stable/.
[3] Fox A. Griffith R. Joseph A. D. Katz R. Knowinski A. Lee G. Patterson D. Rabkin A. Stoic I. Armbust, M. and M. Zaharia. A view of cloud computing. Communications of the ACM, 53:50–58, 2010.
[4] Fay Chang, Jeffrey Dean, Sanjay Ghemawat, Wilson C. Hsieh, Deborah A. Wallach, Mike Burrows, Tushar Chandra, Andrew Fikes, and Robert E. Gru- ber. Bigtable: A distributed storage system for structured data. ACM Trans. Comput. Syst., 26:4:1–4:26, 2008.
[5] Jonathan Cohen. Graph twiddling in a mapreduce world. Computing in Science and Engg., 11:29–41, 2009.
[6] Michael Cusumano. Cloud computing and saas as new computing platforms. Communications of the ACM, Volume 53 Issue 4:27–29, April 2010.
[7] Jeffrey Dean and Sanjay Ghemawat. Mapreduce: simplified data processing on large clusters. Communications of the ACM, Volume 51 Issue 1:107–113, January 2008.
[8] Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung. The google file sys- tem. SIGOPS Operating Systems Review, 37:29–43, 2003.
[9] Toru Hasunuma. Completely independent spanning trees in the underlying graph of a line digraph. Discrete Mathathematica., 234:149–157, 2001.
[10] Toru Hasunuma. Completely independent spanning trees in maximal planar graphs. In Revised Papers from the 28th International Workshop on Graph- Theoretic Concepts in Computer Science, 2002.
[11] G. Lars. HBase: The Definitive Guide. Oreilly and Associates Inc., 2011.
[12] Rob Pike, Sean Dorward, Robert Griesemer, and Sean Quinlan. Interpreting the data: Parallel analysis with sawzall. Sci. Program., 13(4):277–298, October 2005.
[13] H. Ricky. HDFS Map/Reduce Implementation, 2012.
[14] Konstantin Shvachko, Hairong Kuang, Sanjay Radia, and Robert Chansler. The hadoop distributed file system. In Proceedings of the 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST). IEEE Com- puter Society, 2010.
[15] W. Tom and C. Doug. Hadoop: The Definitive Guide, 2nd Edition. Oreilly and Associates Inc., 2010.
[16] Windows. Windows Azure. http://www.windowsazure.com/en-us/.
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