Hydra: A Decentralized File System for Persistent Memory and RDMA Networks

Shengan Zheng, Jingyu Wang, Dongliang Xue, Jiwu Shu, Linpeng Huang
Published in IEEE Transactions on Parallel and Distributed Systems (TPDS), 2022

Abstract: Emerging byte-addressable persistent memory (PM) has the potential to disrupt the boundary between memory and storage. Combined with high-speed RDMA networks, distributed PM-based storage systems offer the opportunity to provide huge increases in storage performance by closely coupling PM and RDMA features. However, existing distributed file systems adopt the conventional centralized client-server architecture designed for traditional disks, leading to excessive access latency, limited scalability, and high recovery overhead. In this paper, we propose a fully decentralized PM-based file system, Hydra. By exploiting the performance advantages of local PM, Hydra leverages data access locality to achieve high performance. To accelerate file transmission among Hydra nodes, file metadata and data are decoupled and updated differentially through one-sided RDMA reads. Hydra also batches RDMA requests and classifies RPCs into synchronous and asynchronous types to minimize network overhead. Decentralization enables Hydra to tolerate node failures and achieve load balancing. Experimental results show that Hydra outperforms existing distributed file systems by a large margin, and shows good scalability on multi-threaded and parallel workloads.

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