在常見的資料格網系統當中，資料密集(Data-intensive)的應用程式通常利用資料預先準備(Pre-staging)機制或者是碎片資料共享機制來存取資料。資料預先準備機制同時地從多路的資料來源下載一個完整的共享檔案，甚至當只有一小塊的檔案片段被需要的時候，明顯地，資料預先準備機制浪費不必要的資料傳送以及儲存空間。相反地，碎片資料共享機制從單一的資料來源下載被需要的資料片段，然而，碎片資料共享機制不能充份利用可用的網路頻寬。因此這篇論文提出了一個利用混合機制的資料共享系統，這個混合機制被稱為需求導向的資料協同配置機制(On-Demand Co-Allocation)。它利用多路的資料串流來同時地抓取被需要的檔案片段，因此它能夠減少資料的傳輸時間、被浪費的網路頻寬，以及所需要的儲存空間。除此之外，它提供了一個透通的方式，讓不知曉格網的應用程式不需修改或重新編譯程式，就可使用格網上的資料。最後、由實驗結果指出，需求導向的協同配置機制能夠在資料格網上減少資料密集應用程式的執行時間。

With the conventional data grid system, a data-intensive application usually accesses data by using either the pre-staging scheme or the fragmented data sharing scheme. The pre-staging scheme simultaneously downloads an entire shared file from multiple data sources even when only a tiny file fragment is required. Obviously, the pre-staging scheme consumes unnecessary data transmission time and storage space. The fragmented data sharing scheme, on the other hand, downloads only the necessary fragments from a single data source. However, the fragmented data sharing scheme does not fully exploit available network bandwidth. Accordingly, this thesis presents a data sharing system which uses a hybrid scheme, designated as the On-Demand Co-Allocation scheme (ODCA). The ODCA scheme uses multiple data streams to concurrently fetch different necessary file fragment, thereby reducing data transmission time, wasted network bandwidth and required storage space. Beside, the ODCA scheme provides a transparency that facilitates the grid-unaware applications can use the Data Grid without any modification or re-compiling. Experimental results show the ODCA scheme successfully reduces turnaround time in data-intensive applications.

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