A Novel Automated Cloud Storage Tiering System through Hot-Cold Data Classification

With information technology growing and rapidly increasing ICT equipment, a massive amount of data have been generated and stored in the cloud. However, the majority of them are infrequently accessed data. Data temperature describes the frequency of data access. Hot storage is dedicated to storing frequently accessed data while cold storage is designed for infrequently accessed data. To cope with the issue of exponential data growth in cloud, it is essential to allocate different categories of data to proper storage media. In this research, we propose an automated cloud storage tiering system for the task of data temperature prediction through hot-cold data classification and data migration, which allocates the predicted categorized data to the corresponding storage media. There are three major contributions in this paper. Firstly, the feasibility: by successfully predicting the infrequent access data and moving them to the cold storage, we obtain significant cost savings. Secondly, the reliability: while having the benefit of storage-cost saving, our proposed system also ensures customers satisfaction by enhancing the ratio of data access through hot storage. Lastly, the flexibility: operational strategy varies from cloud storage service providers. Our system characterizes different scenarios and provides the customized optimal solution.