ACM Transactions on Storage最新文献

Post-deduplication delta compression is a data reduction technique that calculates and stores the differences of very similar but non-duplicate chunks in storage systems, which is able to achieve a very high compression ratio. However, the low throughput of widely used resemblance detection approaches (e.g., N-Transform) usually becomes the bottleneck of delta compression systems due to introducing high computational overhead. Generally, this overhead mainly consists of two parts: ① calculating the rolling hash byte by byte across data chunks and ② applying multiple transforms on all of the calculated rolling hash values.

In this article, we propose Odess, a fast and lightweight resemblance detection approach, that greatly reduces the computational overhead for resemblance detection while achieving high detection accuracy and a high compression ratio. Odess first utilizes a novel Subwindow-based Parallel Rolling (SWPR) hash method using Single Instruction Multiple Data [1] (SIMD) to accelerate calculation of rolling hashes (corresponding to the first part of the overhead). Odess then uses a novel Content-Defined Sampling method to generate a much smaller proxy hash set from the whole rolling hash set and quickly applies transforms on this small hash set for resemblance detection (corresponding to the second part of the overhead).

Evaluation results show that during the stage of resemblance detection, the Odess approach is ∼31.4× and ∼7.9× faster than the state-of-the-art N-Transform and Finesse (a recent variant of N-Transform [39]), respectively. When considering an end-to-end data reduction storage system, the Odess-based system’s throughput is about 3.20× and 1.41× higher than the N-Transform- and Finesse-based systems’ throughput, respectively, while maintaining the high compression ratio of N-Transform and achieving ∼1.22× higher compression ratio over Finesse.

Key–value (KV) software has proven useful to a wide variety of applications including analytics, time-series databases, and distributed file systems. To satisfy the requirements of diverse workloads, KV stores have been carefully tailored to best match the performance characteristics of underlying solid-state block devices. Emerging KV storage device is a promising technology for both simplifying the KV software stack and improving the performance of persistent storage-based applications. However, while providing fast, predictable put and get operations, existing KV storage devices do not natively support range queries that are critical to all three types of applications described above.

In this article, we present KVRangeDB, a software layer that enables processing range queries for existing hash-based KV solid-state disks (KVSSDs). As an effort to adapt to the performance characteristics of emerging KVSSDs, KVRangeDB implements log-structured merge tree key index that reduces compaction I/O, merges keys when possible, and provides separate caches for indexes and values. We evaluated the KVRangeDB under a set of representative workloads, and compared its performance with two existing database solutions: a Rocksdb variant ported to work with the KVSSD, and Wisckey, a key–value database that is carefully tuned for conventional block devices. On filesystem aging workloads, KVRangeDB outperforms Wisckey by 23.7× in terms of throughput and reduce CPU usage and external write amplifications by 14.3× and 9.8×, respectively.

Persistent memory (PM) disaggregation significantly improves the resource utilization and failure isolation to build a scalable and cost-effective remote memory pool in modern data centers. However, due to offering limited computing power and overlooking the bandwidth and persistence properties of real PMs, existing distributed transaction schemes, which are designed for legacy DRAM-based monolithic servers, fail to efficiently work on the disaggregated PM. In this article, we propose FORD, a Fast One-sided RDMA-based Distributed transaction system for the new disaggregated PM architecture. FORD thoroughly leverages one-sided remote direct memory access to handle transactions for bypassing the remote CPU in the PM pool. To reduce the round trips, FORD batches the read and lock operations into one request to eliminate extra locking and validations for the read-write data. To accelerate the transaction commit, FORD updates all remote replicas in a single round trip with parallel undo logging and data visibility control. Moreover, considering the limited PM bandwidth, FORD enables the backup replicas to be read to alleviate the load on the primary replicas, thus improving the throughput. To efficiently guarantee the remote data persistency in the PM pool, FORD selectively flushes data to the backup replicas to mitigate the network overheads. Nevertheless, the original FORD wastes some validation round trips if the read-only data are not modified by other transactions. Hence, we further propose a localized validation scheme to transfer the validation operations for the read-only data from remote to local as much as possible to reduce the round trips. Experimental results demonstrate that FORD significantly improves the transaction throughput by up to 3× and decreases the latency by up to 87.4% compared with state-of-the-art systems.

Most solid-state drives (SSDs) adopt an on-board Dynamic Random Access Memory (DRAM) to buffer the write data, which can significantly reduce the amount of write operations committed to the flash array of SSD if data exhibits locality in write operations. This article focuses on efficiently managing the small amount of DRAM cache inside SSDs. The basic idea is to employ the visibility graph technique to unify both temporal and spatial locality of references of I/O accesses, for directing cache management in SSDs. Specifically, we propose to adaptively generate the visibility graph of cached data pages and then support batch adjustment of adjacent or nearby (hot) cached data pages by referring to the connection situations in the visibility graph. In addition, we propose to evict the buffered data pages in batches by also referring to the connection situations, to maximize the internal flushing parallelism of SSD devices without worsening I/O congestion. The trace-driven simulation experiments show that our proposal can yield improvements on cache hits by between 0.8% and 19.8%, and the overall I/O latency by 25.6% on average, compared to state-of-the-art cache management schemes inside SSDs.

To satisfy the enormous storage capacities required for big data, data centers have been adopting high-density shingled magnetic recording (SMR) disks. However, the weak fine-grained random write performance of SMR disks caused by their inherent write amplification and unbalanced read–write performance poses a severe challenge. Many studies have proposed solid-state drive (SSD) cache systems to address this issue. However, existing cache algorithms, such as the least recently used (LRU) algorithm, which is used to optimize cache popularity, and the MOST algorithm, which is used to optimize the write amplification factor, cannot exploit the full performance of the proposed cache systems because of their inappropriate optimization objectives. This article proposes a new SMR-aware cache framework called SAC+ to improve SMR-based hybrid storage. SAC+ integrates the two dominant types of SMR drives—namely, drive-managed and host-managed SMR drives—and provides a universal framework implementation. In addition, SAC+ integrally combines the drive characteristics to optimize I/O performance. The results of evaluations conducted using real-world traces indicate that SAC+ reduces the I/O time by 36–93% compared with state-of-the-art algorithms.

The migration of traditional desktop and server applications to the cloud brings challenge of high performance, high reliability and low cost to the underlying cloud storage. To satisfy the requirement, this paper proposes a hybrid cloud-scale block storage system called Ursa. Trace analysis shows that the I/O patterns served by block storage have only limited locality to exploit. Therefore, instead of using SSDs as a cache layer, Ursa proposes an SSD-HDD-hybrid storage structure that directly stores primary replicas on SSDs and replicates backup replicas on HDDs. At the core of Ursa’s hybrid storage design is an adaptive journal that can bridge the performance gap between primary SSDs and backup HDDs for random writes, by transforming small backup writes into journal appends which are then asynchronously replayed and merged to backup HDDs. To efficiently index the journal, we design a novel range-optimized merge-tree (ROMT) structure that combines a continuous range of keys into a single composite key {offset,length}. Ursa integrates the hybrid structure with designs for high reliability, scalability, and availability. Experiments show that Ursa in its hybrid mode achieves almost the same performance as in its SSD-only mode (storing all replicas on SSDs), and outperforms other block stores (Ceph and Sheepdog) even in their SSD-only mode while achieving much higher CPU efficiency (IOPS and throughput per core).

No abstract available.