Zoned Namespace (ZNS) is an emerging SSD interface with great potential for performance and cost in largescale cloud SSD deployments. Enabling in-storage hardware compression on ZNS SSDs is promising for further enhancing the cost-effectiveness of ZNS-based storage infrastructures. However, based on our investigation, existing solutions based on the host-transparent methodology are sub-optimal on ZNS SSDs due to two intrinsic challenges: (1) locating compressed chunks and (2) harvesting space savings from compression.In this paper, we for the first time revisit the compression storage system architecture on the emerging ZNS SSD and propose to decouple compression execution with indexing. We propose CCZNS (CC: collaborative compression), an advanced ZNS interface that revives in-storage hardware compression on ZNS SSDs through a novel host-SSD collaborative approach. We present how CCZNS can benefit host software by performing a case study on RocksDB and ZenFS. Extensive experiments demonstrate that the CCZNS-based storage system significantly outperforms existing system solutions.

The legacy block interface for I/O benefits from data locality but faces challenges with I/O amplification due to the frequent small read-write operations common in most applications. Dual-Interface SSDs, which integrate block-interface Flash memory with byte-addressable memory, create opportunities for application redesign by reducing unnecessary read-write amplification on storage devices. However, current Dual-Interface SSD hardware remains limited in terms of size and functionality. Existing software designs for Dual-Interface SSDs often use the byte-addressable space as sequential logs with basic batch reclamation. While this space allows random access, and batch reclamation introduces significant tail latency caused by excessive read and write-back operations. To fully exploit the potential of Dual-Interface SSDs, we have developed a prototype on a hardware-software configurable platform. This publicly accessible Dual-Interface SSD offers realistic and optimized performance, overcoming the size and functionality limitations of previous designs. In addition, we demonstrate the ability of Dual-Interface SSDs to reduce write amplification in a traditional Copy-on-Write B-Tree data store. By employing two key techniques–Tree Pointer Relocation, which decouples indirection from the tree structure, and Tree Node Accommodation, which enables small-sized key-value pair updates to be processed directly in the byte-addressable space–we significantly improve the efficiency of storage operations. Our evaluation reveals that these techniques, when applied to Dual-Interface SSDs, achieve performance gains of 30.5% and 52.5% compared with a CoW B-Tree operating on traditional block-interface SSDs.

The zoned storage has revolutionized the decades-old block storage in lowering the cost-per-gigabyte while enabling the host system to achieve better performance. With such benefit of cost and performance, we still require careful consideration on the endurance when deploying the applications on the zoned storage, since the modern storage tends to trade its endurance for larger capacity at lower cost. In this regard, although previous studies have deployed the log-structure merge (LSM-tree)-based key-value (KV) store on the zoned storage, the LSM-tree-based KV store can be suboptimal choice to build a cost-effective KV store on zoned storage, since LSM-tree has a well-known problem of write amplification (WA). Therefore, based on the key insight that the Fragmented Log-Structured Merge tree (FLSM-tree) substantially alleviates the notorious write amplification problem of the classical LSM-tree and inherently complies with the sequential write constraint of zoned storage, FLSM-tree would be a promising design choice to build a cost-effective KV store on zoned storage. However, based on our investigation, deploying an FLSM-tree-based KV store on zoned storage faces two challenges: The write amplification of the host-initiated garbage collection (GC) cancels out the low WA merit of FLSM-tree, and FLSM-tree results in high space amplification to increase the cost. In this regard, this article presents ZonesDB , a novel FLSM-tree-based KV store that comes with a series of innovative “zone-aware” techniques for pursuing write optimality and space adaptability. Our evaluations, based on two types of production-grade zoned storage (i.e., ZNS SSD and HM-SMR HDD), reveal that ZonesDB can bring into play the low WA merit of FLSM-tree, deliver outstanding write performance, and mitigate the space amplification problem of FLSM-tree on zoned storage.

Fully-external graph computation systems exhibit optimal scalability by computing the ever-growing, large-scale graph with a constant amount of memory on a single machine. In particular, they keep the entire massive graph data in storage and iteratively load parts of them into memory for computation. Nevertheless, despite the merit of optimal scalability, their unreasonably-low efficiency often makes them uncompetitive, and even unpractical, to the other types of graph computation systems. The key rationale is that most existing fully-external graph computation systems over-emphasize retrieving graph data from storage through sequential access. Although this principle achieves high storage bandwidth, it often causes reading excessive and irrelevant data, which can severely degrade their overall efficiency. Therefore, this work presents Seraph, a fully-external graph computation system that achieves optimal S calability while toward satisfactory E fficiency improvement. Particularly, inspired by the modern storage offering comparable sequential and random access speeds, Seraph adopts the principle of on-demand processing to access the necessary graph data for saving I/O while enjoying the decent speed in random access. On the basis of this principle, Seraph further devises three practical designs to bring excellent performance leap to fully-external graph computation: 1) the hybrid format to represent the graph data for striking a good balance between I/O amount and access locality, 2) the vertex passing to enable efficient vertex updates on top of hybrid format, and 3) the selective pre-computation to re-use the loaded data for I/O reduction. Our evaluations reveal that Seraph notably outperforms other state-of-the-art fully-external systems under all the evaluated billion-scale graphs and representative graph algorithms by up to two orders of magnitude.

Zoned Namespace (ZNS) is an emerging interface that shows great promise for high-density and low-cost cloud environment deployments. Modern high-density SSDs, on the other hand, typically use a hybrid SLC/QLC architecture to mitigate the deficiencies of QLC. Unfortunately, simply integrating ZNS with the mainstream host-transparent hybrid architecture would lead to substantial performance and endurance overhead, defeating the purpose of this architecture in the first place.

LSM-tree based Key-value (KV) stores greatly fit the needs of write-intensive applications with its efficient data store and retrieval operations to datasets. To accommodate ever-growing datasets, shingled magnetic recording (SMR) drives have become a popular option to provide large storage capacity for KV stores at low cost. SMR drives achieve high storage density via overlapping tracks on the disk surface. However, the overlapped track layout induces the sequential-write constraint and prevents KV stores from storing and rearranging KV pairs efficiently. In this paper, we present KVSTL, a KV store aware Shingled Translation Layer (STL), to preserve the merits of existing KV stores, while exploiting the high storage density of SMR drives. KVSTL is proposed as an application support to hide the management complexity of SMR drives and facilitates the management of SMR drives via passing only the “level” and “invalidation” information of LSM-tree based KV stores onto SMR drives. The proposed KVSTL achieves its performance enhancement via managing key-value pairs with level awareness and enabling efficient storage space management with the invalidation information. The results show that KVSTL can reduce the written data amount for 69.45 percent on average and the latency for up to 62.72 percent when compared with SMR-based LevelDB.

The past decades have witnessed the tremendous success of Conventional Magnetic Recording (CMR)-based Hard Disk Drives (HDDs) in data storage. To eliminate the bottleneck of CMR-based HDDs in providing higher areal density, an emerging Interlaced Magnetic Recording (IMR) is capable of achieving higher areal density with limited changes to disk makeup. Nevertheless, existing approaches for IMR-based HDDs may suffer serious read and write performance degradation as compared with CMR-based HDDs. Thus, this article presents a device-level solution, namelyMAGICtranslation layer, which aims atMAkinGIMR-based HDDs perform likeCMR-based HDDs in terms of comparable access performance. Specifically, not merely trying to improve the performance of raw IMR-based HDDs, this work, for the first time, moves one step forward to minimize the performance gap between IMR and CMR-based HDDs. Technically, by 1) fully utilizing two special CMR-like potentials of IMR and 2) gracefully trading the sequential access performance as space usage increases, MAGIC minimizes track rewriting overheads to achieve CMR-like performance. Our results reveal that MAGIC not only improves the write performance compared with existing designs, but also has potential to approach read and write performance of CMR-based HDD.

Emerging Interlaced Magnetic Recording (IMR) technology delivers higher areal density for hard drives by interlacing tracks. However, to update the interlaced tracks overlapped by valid data, IMR must entail extra I/Os to rewrite tracks, and the typical read-modify-write (RMW) update strategy further amplifies extra I/Os and deteriorates write performance for ensuring crash consistency. In contrast to the state-of-the-art designs that focus on reducing the occurrence of RMW(s), this paper presents a novel efficient-yet-crash-consistent update strategy, namely move-on-modify (MOM), to completely substitute the typical RMW. By moving data upon updating tracks, MOM essentially circumvents the redundant data restorations of RMW with the ensured crash consistency, and simultaneously takes advantage of these data movements to properly migrate data for reducing the probability of incurring track rewrites without taking extra data migrations. Evaluation results show that MOM not only effectively boosts write performance by at least 77.89% via minimizing extra I/Os, but also greatly benefits the state-of-the-art designs.