Abstract
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.