Informed data distribution selection in a self-predicting storage system

Abstract

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.