UC Santa Cruz

Storing large amounts of data for different users has become the new normal in a modern distributed cloud storage environment. Storing data successfully requires a balance of availability, reliability, cost, and performance. Typically, systems design for this balance with minimal information about the data that will pass through them. We propose a series of methods to derive groupings from data that have predictive value, informing layout decisions for data on disk. Unlike previous grouping work, we focus on dynamically identifying groupings in data that can be gathered from active systems in real time with minimal impact using spatiotemporal locality. We outline several techniques we have developed and discuss how we select particular techniques for particular workloads and application domains. Our statistical and machine-learning-based grouping algorithms answer questions such as “What can a grouping be based on?” and “Is a given grouping meaningful for a given application?” We design our models to be flexible and require minimal domain information so that our results are as broadly applicable as possible. We intend for this work to provide a launchpad for future specialized system design using groupings in combination with caching policies and architectural distinctions such as tiered storage to create the next generation of scalable storage systems.