A Case for Asymmetric Non-Volatile Memory Architecture

TL;DR

This paper proposes an asymmetric NVM architecture that decouples servers from persistent storage, leveraging RDMA for resource disaggregation, and demonstrates high performance and scalability in data management.

Contribution

It introduces a novel asymmetric NVM architecture with a framework that enables efficient, high-performance persistent data structures using RDMA and operation logs.

Findings

Achieves comparable performance to symmetric architectures.

Speedup of 6-22x with proposed optimizations.

Supports multiple data structures and applications efficiently.

Abstract

The byte-addressable Non-Volatile Memory (NVM) is a promising technology since it simultaneously provides DRAM-like performance, disk-like capacity, and persistency. The current NVM deployment is symmetric, where NVM devices are directly attached to servers. Due to the higher density, NVM provides larger capacity and can be shared among servers. Unfortunately, in the symmetric setting, the availability of NVM devices is affected by the specific machine it is attached to. High availability can be realized by replicating data to NVM on a remote machine. However, it requires full replication of data structure in local memory, limiting the size of the working set. This paper rethinks NVM deployment and makes a case for the asymmetric NVM architecture, which decouples servers from persistent data storage. In the proposed AsymNVM architecture, NVM devices (back-end nodes) can be shared by multiple servers (front-end nodes) and provide recoverable persistent data structures. The asymmetric architecture is made possible by RDMA, and follows the recent industry trend of resource disaggregation. We build AsymNVM framework based on AsymNVM architecture that implements: 1) high performance persistent data structure update; 2) NVM data management; 3) concurrency control; and 4) crash-consistency and replication. The central idea is to use operation logs to reduce the stall due to RDMA writes and enable efficient batching and caching in front-end nodes. To evaluation performance, we construct eight widely used data structures and two applications based on AsymNVM framework, and use traces of industry workloads. In a cluster with ten machines, the results show that AsymNVM achieves comparable performance to the best possible symmetric architecture while avoiding all the drawbacks with disaggregation. Compared to the baseline AsymNVM, speedup brought by the proposed optimizations is 6~22x.