An End-to-End Learning-Based Metadata Management Approach for Distributed File Systems

Current distributed file systems are designed to support PB-scale even EB-scale data storage. Metadata service, which manages file attribute information and the global namespace tree, is crucial to system performance. Distributed metadata management, using multiple metadata servers (MDS's) to store metadata, provides effective approaches to alleviate the workload of a single server. However, maintaining good metadata locality and keeping load balancing among MDS's at the same time is a nontrivial problem. To better take advantage of the current distribution of the metadata, in this article, we present the first machine learning based model called DeepHash, which leverages the neural network to learn a locality preserving hashing (LPH) mapping scheme. DeepHash first converts the metadata nodes to feature vectors by the network embedding technology. Due to the absence of training labels, i.e., the hash values of metadata nodes, we design a pair loss function with distinctive characters to train DeepHash, and introduce the sampling strategy to improve the training efficiency. Besides, we propose an efficient algorithm to dynamically balance the workload and adopt the cache model to improve query efficiency. The experiments on the Amazon EC2 platform demonstrate that the DeepHash can preserve the metadata locality meanwhile maintaining a high load balancing, which denotes the effectiveness and efficiency of DeepHash compared with traditional and state-of-the-art schemes.