Frequency and Similarity-Aware Partitioning for Cloud Storage Based on Space-Time Utility Maximization Model

With the rise of various cloud services, the problem of redundant data is more prominent in the cloud storage systems. How to assign a set of documents to a distributed file system, which can not only reduce storage space, but also ensure the access efficiency as much as possible, is an urgent problem which needs to be solved. Space-efficiency mainly uses data de-duplication technologies, while access-efficiency requires gathering the files with high similarity on a server. Based on the study of other data de-duplication technologies, especially the Similarity-Aware Partitioning （SAP） algorithm, this paper proposes the Frequency and Similarity-Aware Partitioning （FSAP） algorithm for cloud storage. The FSAP algorithm is a more reasonable data partitioning algorithm than the SAP algorithm. Meanwhile, this paper proposes the Space-Time Utility Maximization Model （STUMM）, which is useful in balancing the relationship between space-efficiency and access-efficiency. Finally, this paper uses 100 web files downloaded from CNN for testing, and the results show that, relative to using the algorithms associated with the SAP algorithm （including the SAP-Space-Delta algorithm and the SAP-Space-Dedup algorithm）, the FSAP algorithm based on STUMM reaches higher compression ratio and a more balanced distribution of data blocks.