臺灣博碩士論文加值系統

隨著科技不斷的進步，行動裝置不斷推出新機種，讓使用者願意購買去體驗在軟硬體方面的效能，多年過後，就可能累積不同運算能力的行動裝置了。
在此研究中，我們將使用多個異質行動裝置和一個無線寬頻分享器組成一個MapReduce架構，透過這個架構不只可以控制各個裝置，也可以在單機或多機裝置上執行不同的應用程式。MapReduce架構結合使用多Thread做平行運算與負載平衡的方法，與任一裝置相較之下效能有所改善。在實驗中，我們除了在4種不同類型的行動裝置上執行兩個應用程式去統計字數和質數外，也在電腦上執行，並以此當作基準來比較，根據實驗的結果證明可行性和針對異質性行動裝置的MapReduce架構的效率。

With the advance of science and technology, mobile devices continue to introduce new models, so that users are willing to buy to experience in hardware and software performance. After some years, users could accumulate different computing capability of mobile devices. In the thesis, we will use heterogeneous mobile devices and a wireless router to build a MapReduce framework. Through the MapReduce framework, we not only can control each mobile device but also execute different applications in single mobile device or multiple mobile devices. The MapReduce framework can combine a multi-thread parallel computing with a load balance method to improve the performance when compared to any single mobile device. In the experiments, we will run two applications to count word and prime numbers under 4 different types of mobile devices. We will also run the two applications on a PC as a baseline comparison. According to the experimental results, we can demonstrate the feasibility and efficiency of the MapReduce framework for heterogeneous mobile devices.

第1章 簡介
第2章 相關研究
第3章 動機
第4章 背景知識
4.1 Android
4.2 MapReduce
第5章 實現 - 異質性行動裝置的MapReduce架構
5.1 概論
5.1.1 開發環境的操作與設定
5.1.2 簡要的運作流程
5.2 系統功能
5.3 整體架構
5.4 運作流程
5.4.1 單機運算運作流程
5.4.2 多機運算運作流程
5.5 負載平衡 (Load balance)
5.6 實現過程中遇到的問題
5.6.1 多Thread搶IO問題
5.6.2 Socket UDP
5.6.3 重複運算檔案的問題
第6章 效能測試
6.1 使用設備
6.2 運算功能
6.3 測試方式
6.4 測試結果
6.4.1 字數統計
6.4.2 質數
第7章 結論
第8章 參考文獻

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