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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/94532

    Title: 基於節能考量系統差易環境下最佳虛擬機器指派之研究
    Other Titles: Power saving of virtual machine assignment research based on different performance of virtual machine distribution
    Authors: 吳銘智;Wu, Ming-Zhi
    Contributors: 淡江大學電機工程學系碩士班
    李維聰;Lee, Wei-Tsong
    Keywords: 分散式運算;評分機制;雲端網路;MapReduce;Benchmark;Cloud Network
    Date: 2013
    Issue Date: 2014-01-23 14:44:43 (UTC+8)
    Abstract: 本論文提出了Green MapReduce System(GMS)的系統架構,主要是在解決Load Balance負載平衡以及Power Saving節能的部分。下列提出三點改進方法:一、提出Green Master架構。二、為群組內的伺服器加上Benchmark Score評分值。三、提出演算法關於如何區別高分數的伺服器以及低分數的伺服器,以及如何最有效率的運用運算資源而不造成浪費。
    本論文是基於Hadoop的MapReduce系統做出改進,Hadoop是一套由Google MapReduce系統演化出來的的開放性軟體,可以讓使用者利用安裝此軟體而互相建立連線,獲得大量運算資源,之後再透過撰寫Map Function以及Reduce Function決定運算的目的以及執行方法。但是通常為了獲得群組內最大的運算資源,往往會使群組內的所有伺服器始終處於開機狀態或是保持在高速運轉的狀態,這在無形中會造成不必要的浪費。例如:電腦性能的好壞不一,運算速度自然不同,若是分配相同的工作量給所有的運算伺服器勢必會造成某些伺服器的工作提早結束,但是還要等待其他運算速度較差的伺服器完成工作,這段時間就會空轉造成資源上的浪費;又例如:群組伺服器性能相近,但是隨著時間的推移,速度較慢的伺服器的工作會慢慢累積,又會造成上述例子的情況再一次發生。
    MapReduce is a kind of distributed computing system, and also many people use it nowadays. In this paper, the Green Master based on MapReduce is proposed to solve the problem between load balance and power saving. There are three mechanism proposed by this paper to improve the MapReduce system efficiency. First, a brand new architecture called Green Master is designed in the system. Second, Benchmark Score is added to each service in the cluster. In the last, an algorithm about how to distinguish the high score service and the low score service, and explain how to use them effectively.
    The algorithm in this paper will be used to improve the system efficiency based on MapReduce of Hadoop. Hadoop is a kind of open source software that develop from Google MapReduce, and it can will create a cluster that connects each services. The cluster is used to make more computing resources called computing pool, and it can be expanded more and more. In the end, we can decide what we want to get or how to execute the program through coding the Map Function and Reduce Function. As usual, in order to make the maximum computing resources, the services must keep the high-speed state, but it also has a lot of unnecessary waste. For example, service performance are not the same, some of them are very high, but some of them are very low. if we allocate the same amount of work to all service, it must cause a part of service will complete the work early, but it still have to wait other service that performance is poor, and the waiting time means resources wastes. We will talk about how to make the service off if the performance is too low that seriously affects the system performance.
    Appears in Collections:[電機工程學系暨研究所] 學位論文

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