Towards Granular Data Placement Strategies for Cloud Platforms

Traditional data placement strategies in the context of Information Lifecycle Management (ILM) are applicable only to on-site storage systems. In contrast to this approach, Cloud storage provides a novel possibility to reduce or entirely eliminate capital expenditures for hardware. As a unique solution to buffer short-term resource demand peaks, Cloud infrastructures can be combined with on-site systems to support efficient placement of data. The algorithms underlying this optimization must consider not only the workload as a whole, but rather variable-sized sub workloads to determine an optimal placement. As a means to identify these sub workloads, we introduce a multi-dimensional granularization approach. Based on different granules of metadata information, we propose a flexible hybrid data placement system incorporating both on-site and Cloud resources.