Understanding NVMe Zoned Namespace (ZNS) Flash SSD Storage Devices

TL;DR

This paper analyzes the integration of NVMe Zoned Namespace (ZNS) SSDs into host systems, examining software support, performance implications, and configuration considerations for optimizing workload performance.

Contribution

It provides a systematic analysis of ZNS integration options, reports on Linux Kernel support, and offers initial performance measurements highlighting key factors affecting performance.

Findings

Larger I/O sizes are needed to saturate ZNS bandwidth.

I/O scheduler configuration impacts workload-dependent performance.

Careful ZNS integration and configuration are essential for optimal performance.

Abstract

The standardization of NVMe Zoned Namespaces (ZNS) in the NVMe 2.0 specification presents a unique new addition to storage devices. Unlike traditional SSDs, where the flash media management idiosyncrasies are hidden behind a flash translation layer (FTL) inside the device, ZNS devices push certain operations regarding data placement and garbage collection out from the device to the host. This allows the host to achieve more optimal data placement and predictable garbage collection overheads, along with lower device write amplification. Thus, additionally increasing flash media lifetime. As a result, ZNS devices are gaining significant attention in the research community. However, with the current software stack there are numerous ways of integrating ZNS devices into a host system. In this work, we begin to systematically analyze the integration options, report on the current software support for ZNS devices in the Linux Kernel, and provide an initial set of performance measurements. Our main findings show that larger I/O sizes are required to saturate the ZNS device bandwidth, and configuration of the I/O scheduler can provide workload dependent performance gains, requiring careful consideration of ZNS integration and configuration depending on the application workload and its access patterns. Our dataset and code are available at https: //github.com/nicktehrany/ZNS-Study.