Parametric Optimization of Storage Systems

Most storage systems come with large set of parameters to directly or indirectly control a specific set of metrics that may include performance, energy, etc. Often, storage systems are deployed with default configurations, rendering them sub-optimal. Finding optimal configurations is difficult due to the numerous combinations of parameters and parameter sensitivity to workloads and deployed environments. Previous research on parameter optimization was either limited to narrow problems or not widely applicable to storage stack parameter optimization in general. Based on promising early results, we propose using meta-heuristic techniques such as genetic algorithms to efficiently find nearoptimal configurations for storage systems.