Computational solid-state drives ( CompSSDs ) are evolving into intelligent storage solutions that integrate near-data computing architectures to achieve computation-storage convergence. They can reduce the overhead of data migration and then accelerate the execution of data-centric workloads through in-situ processing. In the context of CompSSDs , the data cache must buffer both host-side output data and SSD -side input data, making efficient cache management a critical challenge. Existing cache partitioning approaches for CompSSDs typically assume mandatory caching of all host-side writes, resulting in cache pollution from cold data and severe resource contention. To address this issue, this paper proposes ByPASS , a novel cache management scheme that dynamically co-optimizes write bypass and cache partitioning through periodic runtime decisions. Specifically, at each decision window, ByPASS analyzes I/O access patterns, cache hit ratios, and host- CompSSD transfer latency, to determine a unified policy for the current time window, either bypass all host writes directly to NAND flash or dynamically adjust the cache allocation ratio between host writes and CompSSD task inputs. We implement ByPASS in the MQSim simulator and evaluate its effectiveness by replaying I/O traces from real-world applications. Our proposal can reduce I/O response time by 6.4% – 34.1% , in contrast to state-of-the-art cache management schemes.
Zoned Namespace (ZNS) SSDs offer a promising approach to simplifying flash management and reducing storage costs. Enabling built-in compression in ZNS SSDs can further enhance storage efficiency and improve performance without increasing the host’s burden. However, compression introduces variable-size data, disrupting the alignment between logical and physical page addresses. This misalignment, together with ZNS SSDs’ small internal DRAM resources, introduces three fundamental challenges: (1) efficiently locating the physical address of compressed data at low memory usage, (2) deterministically addressing multiple compressed logical pages packed within a physical page without maintaining large per-logical-page metadata, and (3) avoiding severe performance degradation caused by serialized reads when compressed data spans multiple physical pages without incurring large memory overhead. To address these challenges, we propose Lemonade , a ZNS SSD design with built-in compression that enables efficient compact storage while maintaining low memory overhead. The design is based on three key insights driven by the unique characteristics of ZNS SSDs. First, we observe that the sequential write and append-only constraints of ZNS naturally yield an approximately linear LPN-PPN mapping after compression. Lemonade exploits this by employing lightweight learned linear models, enabling physical address lookup with low memory cost (8 bytes per model). Second, in ZNS SSDs, compressed logical pages packed within a physical page have consecutive logical page numbers, and each expands to a fixed-size logical page after decompression. Lemonade exploits this to eliminate heavy metadata for intra-page offsets. Instead, it determines the location of compressed data by small metadata and simple arithmetic. Third, Lemonade mitigates the read latency penalty caused by accessing cross-page data through a scheduling strategy that overlaps the corresponding read requests. This strategy does not introduce additional memory overhead, as it exploits the ZNS SSD’s physical layout where physical pages containing cross-page data are typically placed on different channels and dies. We implement Lemonade on the FEMU ZNS SSD emulator and evaluate it with real-world datasets and storage workloads. Experimental results demonstrate that Lemonade achieves both compact storage and high performance while maintaining low DRAM usage. Lemonade delivers up to 51.1% higher write throughput and 22.2% higher read throughput than the state-of-the-art design. Meanwhile, it significantly reduces the storage footprint and requires only 8 bytes of DRAM per zone for indexing in the best case.
Erasure coding is widely adopted to maintain data reliability, yet it introduces a significant update penalty. We analyze real-world traces and observe several challenges that are not addressed by existing studies, which thereby restricts the performance gains. We propose FastUpdate , an efficient multi-stripe updates framework that assists existing update schemes for fast updates. FastUpdate comprises three key designs: (i) it perceives the update locality and carefully merges multiple update requests accessing the same stripe to reduce the incurred network traffic; (ii) it abstracts the existing update schemes into collector selection and tree construction, greedily generates the update solution for each stripe to balance the transmission load across nodes; (iii) it dynamically schedules appropriate stripes to update in heterogeneous and dynamic networks to fully utilize the bandwidth resources. Comprehensive evaluations verify the effectiveness of FastUpdate on Alibaba ECS. It can increase the update throughput by 16.15%-88.71% for various update schemes.
Emerging Zoned Namespace (ZNS) SSDs transfer garbage collection (GC) to the host, thereby providing more stable performance and extending device life. Although the ZNS SSD has these hardware advantages, each zone in the ZNS SSD must be written sequentially. File systems always need to implement additional mechanisms (IO Scheduling or modifications to the driver/interface) to meet the sequential write constraint of the ZNS SSD, while fully utilizing the high bandwidth of the ZNS SSD under the constraint. These additional mechanisms usually bring extra overhead or limitations and can also cause the scalability problem for the file system. Therefore, in the past, it was naturally assumed that the sequential write constraint of the ZNS SSD would complicate the design of the file system and have a negative impact on file system scalability. However, in this paper, we argue that the previous insight on the impact of the ZNS SSD on file systems is not comprehensive enough. We regard the ZNS interface as a double-edged sword for file system scalability and found that the sequential write constraint of the ZNS device also has a positive impact on file system scalability. Specifically, order-preserving write is essential for crash consistency in file systems but is the scalability bottleneck of file systems that are compatible with SSDs that support Zoned Namespaces. The analysis in this paper indicates that the sequential write constraint of the ZNS device provides an excellent opportunity to simplify and optimize the order-preserving write mechanism in file systems. Based on the new insight, we propose a scalable ZNS-compatible file system called ScaleZNFS. Contrary to previous work that only focuses on mitigating the limitations imposed by the ZNS interface on the file system, ScaleZNFS not only mitigates the negative impact of the ZNS interface on file system scalability through its multi-head logging design and the per-log IO scheduler, but also employs the ZNS-driven order-preserving write mechanism, which can leverage the sequential write constraint of the ZNS interface, to eliminate the scalability bottleneck associated with the order-preserving write. ScaleZNFS outperforms F2FS and XFS by up to 5.05 × and 2.94 ×, respectively, and it can maintain stable scalability with increasing threads.
Consumer flash storage uses block interfaces, leading to performance and endurance issues like large mapping table overhead and write amplification. While zoned abstraction solves these problems in enterprise devices, it introduces new challenges for consumer devices with limited volatile memory and suboptimal file system design. These challenges highlight the need for a better understanding and new designs for zoned storage in consumer devices. We propose ConZone+, a practical and accurate emulation framework designed for consumer-grade zoned flash storage. Unlike existing enterprise-focused emulators, ConZone+ models unique consumer constraints (e.g., limited mapping caches and write buffers, hybrid flash media) and hybrid mapping. Furthermore, to enable system-level emulation, ConZone+ supports block interface for metadata area in file system and provides several useful scripts to assist users. Finally, ConZone+ introduces several enhancements on the firmware logic, including a configurable per-chip command queue, variable zone sizes, and compatibility with non-power-of-two block sizes. Users can explore the internal architecture and management strategies of consumer-grade zoned flash storage and integrate their optimizations into system software with ConZone+. To evaluate the accuracy of ConZone+, we configure it referencing a mobile phone equipped with zoned flash storage and compare its performance to that. The evaluation results show that the average performance deviations are limited to 12.8% for writes and 10% for reads. In addition, we conduct several case studies using ConZone+ to explore the firmware design and the adaptability of the log-structured features of F2FS and the transaction processing of SQLite.
