Informed data distribution selection in a self-predicting storage system

Systems should be self-predicting. They should continuously monitor themselves and provide quantitative answers to What...if questions about hypothetical workload or resource changes. Self-prediction would significantly simplify administrators´ decision making, such as acquisition planning and performance tuning, by reducing the detailed workload and internal system knowledge required. This paper describes and evaluates support for self-prediction in a cluster-based storage system and its application to What...if questions about data distribution selection.