Efficient and Scalable Metadata Management in EB-Scale File Systems

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Abstract
Efficient and scalable distributed metadata management is critically important to overall system performance in large-scale distributed file systems, especially in the EB-scale era. Hash-based mapping and subtree partitioning are state-of-the-art distributed metadata management schemes. Hash-based mapping evenly distributes workload among metadata servers, but it eliminates all hierarchical locality of metadata. Subtree partitioning does not uniformly distribute workload among metadata servers, and metadata needs to be migrated to keep the load balanced roughly. Distributed metadata management is relatively difficult since it has to guarantee metadata consistency. Meanwhile, scaling metadata performance is more complicated than scaling raw I/O performance. The complexity further rises with distributed metadata. It results in a primary goal that is to improve metadata management scalability while paying attention to metadata consistency. In this paper, we present a ring-based metadata management mechanism named Dynamic Ring Online Partitioning (DROP). It can preserve metadata locality using locality-preserving hashing, keep metadata consistency, as well as dynamically distribute metadata among metadata server cluster to keep load balancing. By conducting performance evaluation through extensive trace-driven simulations and a prototype implementation, experimental results demonstrate the efficiency and scalability of DROP.

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