ZNSlice: Partitioning Large-Zone Commodity ZNS SSDs for Multi-Tenancy Sharing
Year: 2026
Type: article
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.
Source: ACM Transactions on Storage
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7170472890