製作大數據的服務往往需要有大數據的儲存與傳輸系統。本論文目標是製作可以具有一套通用client端Application Programming Interface(API)，內部有一套分散式儲存系統以及可以藉有該系統操作外部檔案系統，在兩不同的檔案系統間傳輸檔案的分散式系統。因為具有通用client 端API，所以可易於直接在The Data Relaying Service(DRS) 頂層另外開發系統，像是在頂層架一層file system framework,就變成一套file system。或是頂層架一層運算平台(ex: Massively R Data Parallel Computation over Hadoop without MapReduce[1])。而為了不影響頂層開發的效能以及成本上考量，該系統需要可跨平台，易於擴增以及能易於在頂層另外開發系統。所以製作時可以使用一些可利於開發大數據分析程式的儲存系統(ex: Hadoop Distributed File System (HDFS)[2], HBase[3])。在效能上也不能與外部檔案系統的讀寫上差異太大，且佔用資源也不能太多。

Sometimes to make service of BigData need file systems of BigData. In this paper, our goal is to make distributed system, which has a common API of client, and has a distributed file storage system, and it can operate other file system via itself to transport file between two different file systems. Because of common API of client, users can easily make some services on The Data Relaying Service (DRS). For example, you can make a file system framework on it, so that this service will change to a file system, or you can make a computing platform on it. (Ex: Massively R Data Parallel Computation over Hadoop without MapReduce) In addition, to prevent some problems of cost and efficacy, this service need to can cross platforms, easy amplification, and can make other system on it easily. So, to make it can use some platforms and file systems which are benefit from making system of BigData such as Hadoop Distributed File System (HDFS), HBase. Final, DRS should not differ much from other file systems in efficacy, and should not cost many resources.

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