在資訊爆炸的時代, 資料的增長速度是非常快速的，人們會利用不同的裝置
來獲取我們所需要的資訊，而行動裝置更是人們獲取資訊的一個重要媒介，但每
個行動裝置都有著硬體上的限制例如:CPU、儲存空間、續電力。尤其是當儲存空
間不夠用時，會透過Google Drive, iCloud, Dropbox... 等所提供的雲端空間來存放
資料，但行動裝置上還是儲存了許多不常使用的資料或者重複的資料，所以在此
我們想透過一些方法，讓行動裝置的資源能更有效的利用，所以在此提出了一個
架構稱為: A Deduplication-aware Scalable Storage System for Mobile Device，並透過
這樣的架構來完成資料有效分配、空間可擴張性，相較以往的雲端空間，我們提
供了冷熱資料分析器(hotness filter) 來讓資料分類為較少使用的資料(冷資料) 與經
常使用的資料(熱資料)，並搭配重複資料偵測技術來減少上傳的冷資料時的傳輸
量。最後根據實驗的結果，我們提供了精準的冷熱資料判定和提供行動裝置額外
的使用空間並有效的減少行動裝置上傳所需的傳輸量。

Nowadays, the capacity of data growth is very rapid because of convenient and fast
network transmission and large-capacity storage systems. In particular, mobile devices
are very popular devices to delivery and store information for people. However, mobile
devices have hardware limitations such as slow CPU speed, small storage space, and low
battery capacity. Especially, when the storage space is not enough, cloud storage systems
such as Google Drive, iCloud, and Dropbox can provide extra storage space to store data.
However, the mobile device could still store infrequently used data or duplication data.
In this thesis, we propose a deduplication-aware scalable storage system for mobile devices.
We use a hotness filter to identify the infrequently used data and transfer the data
to the cloud storage. We also design a deduplication mechanism for mobile devices to
decrease the transfer data. According to the experimental results, the proposed method
can efficiently and correctly identify infrequently used data and transfer the data to the
cloud storage to generate extra free space for mobile devices.

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