Using Data Mining for Discovering Patterns in Autonomic Storage Systems

In order to be self-tuning, self-managing, self-healing and selfprotecting, a storage system needs to be able to automatically characterize access patterns. This paper proposes an approach that uses data mining techniques to systematically mine access sequences in a storage system to characterize storage behaviors. More specifically, we use frequent sequence mining algorithms to find block access correlations which can be used to improve the effectiveness of subsystems such as storage caching and disk scheduling, and for disk power management. This paper reports our preliminary results of discovering block correlations from storage access sequences using a recently proposed data mining algorithm called CloSpan.