An Efficient Ring-Based Metadata Management Policy for Large-Scale Distributed File Systems

The growing size of modern file system is expected to reach EB-scale. Therefore, an efficient and scalable metadata service is critical to system performance. Distributed metadata management schemes, which use multiple metadata servers (MDS's) to store metadata, provide a highly effective approach to alleviate the workload of a single server. However, it is difficult to maintain good metadata locality and load balancing among MDS's at the same time. In this paper, we propose a novel hashing scheme called AngleCut to partition metadata namespace tree and serve large-scale distributed storage systems. AngleCut first uses a locality preserving hashing (LPH) function to project the namespace tree into linear keyspace, i.e., multiple Chord-like rings. Then we design a history-based allocation strategy to adjust the workload of MDS's dynamically. Besides, we propose a two-layer metadata cache mechanism, including server-side cache and client-side cache to provide the two stage access acceleration. Last but not least, we introduce a distributed metadata processing 2PC Protocol Based on Message Queue (2PC-MQ) to ensure data consistency. In general, our scheme preserves good metadata locality as well as maintains a high load balancing between MDS's. The theoretical proof and extensive experiments on Amazon EC2 demonstrate the superiority of AngleCut over previous literature.