ZNSlice: Partitioning Large-Zone Commodity ZNS SSDs for Multi-Tenancy Sharing

NVMe SSDs are becoming increasingly larger and faster, making disk partitioning between multiple tenants a prevalent practice. However, co-located applications with diverse I/O patterns often trigger contention within SSD internals. In this work, we explore the potential of large-zone ZNS SSDs, which expose internal states and enable finer-grained controls, to support high-performance disk partitioning in cloud, data center, and local cluster/server environments. To address this, we first conduct a comprehensive inter-operation interference study, examining the interplay between small and large reads/writes on large-zone ZNS SSDs, alongside the impacts of I/O command selection, OPEN zone allocation, and request size manipulation strategies. Second, guided by our five key observations, we propose ZNSlice, which is the first system tailored to optimize the utilization and isolation of large-zone ZNS SSDs for multi-tenancy. It implements operation-level isolation by integrating a suite of optimizations including efficient OPEN zone allocation and management, differentiated read/write handling, zone write mode selection, I/O request size manipulation, and mitigation of background operations overhead. Evaluations using 9 workloads on 2 commodity large-zone SSDs demonstrate that ZNSlice delivers high performance disk partitions via the block interface, natively supporting conventional file systems and existing workloads without modifications. The results confirm that ZNSlice simultaneously achieves significant improvements in multiple metrics, up to 40.2% higher disk utilization, 98.4% better isolation, and 61.4%/71.8% reductions in average/tail latency compared to the best baseline when TPC-C is shared with other workloads.

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Publication Details

Journal
ACM Transactions on Storage
Published
2026-07-24
DOI
https://doi.org/10.1145/3831245
Primary Topic
Advanced Data Storage Technologies
Type
article
Field-Weighted Citation Impact
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article

ZNSlice: Partitioning Large-Zone Commodity ZNS SSDs for Multi-Tenancy Sharing

Q Chen, Jingze Huo, Yinlong Xu, Xiaosong Ma et al.
ACM Transactions on Storage
Advanced Data Storage Technologies
article

ZNSlice: Partitioning Large-Zone Commodity ZNS SSDs for Multi-Tenancy Sharing

Q Chen, Jingze Huo, Yinlong Xu, Xiaosong Ma, Cheng Li, Kai Zhang, Lijun Miao
article en

Abstract

NVMe SSDs are becoming increasingly larger and faster, making disk partitioning between multiple tenants a prevalent practice. However, co-located applications with diverse I/O patterns often trigger contention within SSD internals. In this work, we explore the potential of large-zone ZNS SSDs, which expose internal states and enable finer-grained controls, to support high-performance disk partitioning in cloud, data center, and local cluster/server environments. To address this, we first conduct a comprehensive inter-operation interference study, examining the interplay between small and large reads/writes on large-zone ZNS SSDs, alongside the impacts of I/O command selection, OPEN zone allocation, and request size manipulation strategies. Second, guided by our five key observations, we propose ZNSlice, which is the first system tailored to optimize the utilization and isolation of large-zone ZNS SSDs for multi-tenancy. It implements operation-level isolation by integrating a suite of optimizations including efficient OPEN zone allocation and management, differentiated read/write handling, zone write mode selection, I/O request size manipulation, and mitigation of background operations overhead. Evaluations using 9 workloads on 2 commodity large-zone SSDs demonstrate that ZNSlice delivers high performance disk partitions via the block interface, natively supporting conventional file systems and existing workloads without modifications. The results confirm that ZNSlice simultaneously achieves significant improvements in multiple metrics, up to 40.2% higher disk utilization, 98.4% better isolation, and 61.4%/71.8% reductions in average/tail latency compared to the best baseline when TPC-C is shared with other workloads.

ACM Transactions on Storage
University of Science and Technology of China (CN), Minjiang University (CN), Smart Material (Germany) (DE), Mohamed bin Zayed University of Artificial Intelligence (AE), SMART Reading (US)
Openalex Percentile: Top 6%
Advanced Data Storage Technologies
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