dynamic metadata management for petabyte-scale file systems
In petabyte-scale distributed file systems that decouple read and write from metadata operations, behavior of the metadata server cluster will be critical to overall system performance and scalability. We present a dynamic subtree partitioning and adaptive metadata management system designed to efficiently manage hierarchical metadata workloads that evolve over time. We examine the relative merits of our approach in the context of traditional workload partitioning strategies, and demonstrate the performance, scalability and adaptability advantages in a simulation environment.
dynamic metadata management file systems systems distributed read writing operability behavior server cluster critic performance scalability partition adapt hierarchic workload timing context strategies demonstrate simulation environments scale relation