嵌入式系統為了能大量生產，且方便攜帶或是佈署在任意的空間中，成本多屬低廉，這限制了它們的運算能力和儲存空間等資源，於是在運作中，任何資源上的浪費都是非常嚴重的，其中一個重要的議題就是如何有效利用儲存空間，嵌入式系統並不會使用太過於複雜的file system，但較為簡單的file system對於tail(small) data的處理普遍有空間浪費的問題，它們並不會針對這類型的檔案有特殊處理，而是當作一般檔案來對待，導致這些tail(small) data跟一般檔案一樣占用同樣的空間，原因來自於簡單的file system皆使用cluster作為最小的儲存空間，當要儲存的tail(small) data小於cluster非常多的時候，只會占用cluster一小部分的空間，但cluster剩餘的空間卻無法被其他檔案使用。

對於這樣的問題有一個通用的方式來解決，tail packing技術，該技術可以讓一個cluster儲存多個檔案的tail(small) data，但是它的缺點在嵌入式系統上卻非常明顯，當tail(small) data的size增加時，如果所在的cluster並沒有足夠的額外空間可用，必須另外再找一個有足夠空間的cluster然後將整個tail(small) data的資料搬移到新的cluster，這樣的搬移是非常大的負擔。

我們提出一個dynamic tail packing機制來處理這樣的情況，與傳統的tail packing不同的是，dynamic tail packing允許tail(small) data儲存在多個cluster中，並以特殊的方式形成tail chain，因此，當tail(small) data的size改變的時候，只要在chain中加入新的data即可。

該機制實作在Linux作業系統上，實驗結果顯示出該機制明顯的改善了遷入系統的空間利用率和效能。

Embedded computing systems usually have limited computing power,RAM space, and storage capacity due to the consideration of their cost,energy consumption, and physical size.
Some of them such as sensor nodes and embedded consumer electronics only have a small-sized flash memory as their storage with a (simple) file system to manage their data, which are usually of small sizes. However, the existing file systems usually have low space utilization on managing small files and the tail data of large files. In this work, we propose a dynamic tail packing scheme to optimize the space utilization of file systems by dynamically aggregating/packing the tail data of (small) files together.The proposed scheme was implemented in the file system of Linux operating systems to evaluate its capability. The results demonstrate that the proposed scheme could significantly improve the space utilization of existing file systems.

1. Introduction
1.1. Background
1.2. Paper Organization
2. Background and Research Motivation
2.1. Background
2.2. Research Motivation
3. Dynamic Tail Packing Scheme
3.1. Overview
3.2. Data Structures: mClusters and mCluster Allocation Tables
3.2.1 mDirectory: Directory Modification to Support mCluster
3.2.2 mCluster
3.2.3 mCluster Allocation Table
3.3. mCluster-based Management
3.4. Performance Enhancement with Dynamic Tail Chain
3.5. mCAT Chain Length Limit And The Largest Collection
4. Performance Evaluation
4.1. Experimental Setup
4.2. Experimental Modification
4.3. The Largest Collection of mCAT chain
4.4. Experimental Results
5. Conclusion and Future Work
6. Reference

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