隨著處存需求的逐年增加，雲端處存系統變得越來越重要。雲端供應商提供了巨大但是便宜的處存服務。人們或是公司可以在使用的同時減少了維護硬體及電力的費用。同時公司也能夠將其轉化為自己的服務。對於雲端儲存而言，糾刪碼被用來增進檔案可取得性而且有降低存取檔案時間的潛力。糾刪碼將檔案轉化成許多的片段。這些片段都比原本的檔案小，如此一來可以透過用平行下載的方式來降低下載時間。此外，這些片段也可以放在不同的儲存區域進而降低遇到發生區域毀壞的情況。然而，每個儲存區域都有不同的存取花費，儲存花費跟傳輸花費。有了這些議題，重點就在於如何選擇選擇候選儲存區域來擺放片段以滿足所有要求。在過去的研究中著重於單一的特性，並且不夠實際。在現實世界中需要考量的因素非常的多。在本篇論文中，我們使用了糾刪碼與線性規劃來找到最好的擺放方法。實驗顯示我們的方法可以省下有66%的金錢以及在效能上有50%的提升。

The cloud storage system has been popular recently due to the higher and higher demand of storage space. The cloud providers offer large but cheap storage service. People or companies use them and do not have to pay on hardware or electric utility. Companies can use these features to build its own storage service for benefit too. For cloud storage, erasure code can be used to improve data availability and have potential to reduce download time. Erasure code encode files into chunks and place them in different storage regions for higher availability. Besides, these chunks is smaller than the origin file that the download time can be reduced by using parallel downloading. These chunks can also improve the availability by placed at different regions for avoiding regions failure. However, each region owns different request cost, storage cost or even latency and bandwidth. Besides, users location can largely influence download latency and access cost. With these multiple issues, the main point is how to choose the candidate regions for chunks that can fulfill all requirements. In the past, most thesiss focus on specific features. However, the models from those research are not realistic enough. There are many aspects that we need to take into consideration for being closer to the real world. In this thesis, we propose the method with using erasure code and linear programming to include multiple requirements at the same time and find the best placement strategy. The experiment shows that our work can save money at most 66\% and have at most 50\% performance improvement.

List of Figures 4
1 Introduction 5
2 System Model 8
2.1 Architecture................................ 8 2.2 Assumption ................................ 9
3 Problem Formulation 10
3.1 NotationandAvailability ........................ 10 3.2 DecisionVariables............................. 12 3.3 EvaulatedVariables............................ 12 3.4 Constraints ................................ 13 3.5 ObjectiveFunctions............................ 14
4 Experiment Setup 15
5 Simulation Evaluation 18
5.1 CostMinimization ............................ 18 5.1.1 Popularity&NumberofFile................... 18 5.1.2 FaultToleranceLevel....................... 20 5.1.3 NumberofRegion ........................ 21
5.2 PerformanceMinimization ........................ 23 5.2.1 FaultToleranceLevel....................... 23 5.2.2 NumberofRegion ........................ 24
5.3 Summary ................................. 24
2
6 Real Testbed Experiments 26
6.1 Setup.................................... 26 6.2 DownloadTime.............................. 27 6.3 NumberofRequest&TotalCost .................... 28 6.4 ErasureCode(4,2)TimeProfiling.................... 29 6.5 Summary ................................. 30
7 Related work 31
7.1 TraditionalIssues............................. 31 7.2 ErasureCodeonMulti-cloud....................... 31
8 Conclusion Bibliography
33 34

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