使用雲端平台作為行動裝置之儲存裝置已成為當前常態，但目前仍無法取代本地端儲存裝置。探討主因為傳輸耗時、無法保障傳輸穩定及資料安全，因此行動雲端運算 (Mobile Cloud Computing, MCC) 之研究方向多為改善上述缺點。本文以縮短傳輸耗時為目標，研究發現每次雲端平台傳輸檔案前都須經過Setting-up (SU)步驟，傳輸結束時亦有段Signal-completion (SC)步驟。SU和SC耗時在傳輸大檔案時不明顯，但當使用者開啟多個小檔案或應用程式要求大量瑣碎資料時，重複的SU及SC在整個傳輸流程當中顯得耗時又耗能。本文提出一套基於平台即服務 (PaaS) 之雲端檔案傳輸機制，根據檔案相關性以機器學習 (Machine Learning) 模型為檔案分群，預先傳輸將被開啟之檔案群，減少傳輸檔案時SU與SC的次數達到省時與省能之成果。

Using cloud platform as storage has become current usage habit on mobile device, but it still fail to completely replace local storage due to the the problems of transfer delay, transmission stability and data security. The research in mobile cloud computing (MCC) mostly aim to improve above disadvantages. In this thesis, reducing the transfer delay is our research objective. We found that before cloud platform transmits the file, it must perform a setting-up (SU) step with mobile device. After cloud platform transmits the file, it must also perform a signal-completion (SC) step. For transferring single large file, the time costs of performing SU and SC are not obvious in total transfer time. However, if user or applications request large amount of small files, repeatedly performing SU and SC will be time-consuming and energy-consuming in entire transfer process. Hence, we propose a cloud data transfer mechanism - Related Files Packing Transfer (RFPT), which cluster files according to the relevance among files with machine learning model and pre-transfer the related files to reduce the times of performing SU and SC to achieve the improvement of time-consuming and energy-consuming transfer process.

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