Persistent memory (PM) offers a compelling combination of durability and near-DRAM performance, but it also introduces new challenges for hashing indexes. Existing persistent hashing designs prioritize resizing efficiency at the expense of increased query latency, losing the key advantage of hash tables. This paper introduces$\mathbf{R}^{2}$Hash, a persistent hashing index redesigned from the persistent cache-line hash table, to balance high read performance with efficient resizing.$R^{2}$Hash is guided by a migration rule, enabling it to meet both goals through two main contributions: (i) a cooperative and lowoverhead resizing strategy based on split-order hashing, and (ii) a shift-aware search combined with a two-layer bucket layout that enables lock-free reads with only one PM access on average. Furthermore,$\mathbf{R}^{2}$Hash provides log-free consistency and a nonblocking recovery mechanism. Experimental results demonstrate that$\mathbf{R}^{\mathbf{2}}$Hash achieves up to$8.1 \times$higher search throughput and$7.5 \times$higher insert throughput compared to other persistent hash indexes across a range of workloads.

The Zoned Namespace (ZNS) interface transfers most storage maintenance responsibilities from the underlying Solid-State Drives (SSDs) to the host. This shift creates new opportunities to ensure fairness and high performance in multi-tenant cloud computing environments at both hardware and software levels. However, when applications with different workloads share a single ZNS SSD hardware, traditional fair queueing schedulers fail to achieve fairness due to their limited awareness of workload characteristics. Moreover, allowing multiple outstanding requests to access the device simultaneously improves resource utilization but often leads to significant I/O interference among these requests. This interference results in over-throttling, which subsequently degrades the performance of existing fair queueing schedulers. To address the above problems, this article proposes an efficient and high-performance fair queueing scheduling scheme for ZNS SSD (ZNSFQ) on the host side. Firstly, ZNSFQ introduces a workload-aware fair scheduler that enhances fairness by accurately estimating the I/O cost for each application based on its workload characteristics. Secondly, to optimize performance while ensuring fairness, ZNSFQ designs a request dispatch parallelism adjuster. This adjuster manages the channel-level request dispatch parallelism for each application to minimize I/O interference. Finally, ZNSFQ employs a global adaptive coordinator to alleviate device-level I/O blocking, reducing tail latency and CPU consumption while satisfying fairness and performance. A comprehensive evaluation demonstrates that ZNSFQ significantly enhances fairness and performance compared to the latest fair queuing schedulers. In sequential access scenarios, ZNSFQ enhances fairness by over 38.13% and increases I/O bandwidth by more than 49.24%. Furthermore, in random access scenarios, it reduces CPU utilization by 70.22% while maintaining both fairness and high performance.

Data migration strategies (DMS) improve the overall performance of hybrid memory systems by migrating frequently accessed (hot) data to faster memory. However, designing an efficient DMS is challenging since the key metrics of DMS -hot data selection, migration granularity, and migration frequency -are sensitive to access patterns of workloads. Most existing strategies focus on only one of these metrics and often overlook the crucial impact of access patterns, resulting in sub-optimal performance and unnecessary migration traffic. In this paper, we propose AdaptHM, a fully access-pattern-aware Adaptive data migration strategy for Hybrid Memory systems. AdaptHM achieves adaptability on all three metrics through its unique multi-level data framework. First, AdaptHM adopts a group-level competition policy to select hot blocks, which responds faster to access patterns than threshold-based policies. Second, AdaptHM enables segment-level dynamic migration granularity by decoupling migration from remapping, which shows better access pattern resilience than existing schemes with fixed-size global migration granularity. Third, AdaptHM adjusts the migration frequency at set-level by periodically assessing the migration benefit, avoiding unnecessary migrations. Experimental results demonstrate that AdaptHM improves the performance by an average of 12.78% and reduces energy consumption by up to 37.24% compared to the state-of-the-art scheme.

Persistent Memory (PM) offers both byte-address ability and non-volatility, making it well-suited for accelerating$\mathrm{B}^{+}$-Tree indexes. However, existing persistent$\mathrm{B}^{+}$-Tree indexes face significant performance challenges due to high structural modification operation (SMO) overhead. SMOs often result in costly item migrations and increased tail latency, which severely degrade the overall performance. In this paper, we present SSTree, a high-performance$\mathrm{B}^{+}$– Tree index specifically optimized to address SMO overhead. SSTree introduces three key innovations: (i) efficient leaf node expansion using a list of subnodes to postpone expensive node splits, (ii) delegated fingerprints to speed up search operations across subnodes, and (iii) proactive subnode compaction that employs out-of-place updates to optimize item organization. Our evaluation demonstrates that SSTree delivers up to$4.38\times$higher write throughput and up to$62\times$lower tail latency compared to state-of-the-art persistent$\mathrm{B}^{+}$-Tree indexes.

Emerging Zoned Namespace (ZNS) SSDs divide address space into sequentially written zones and transfer garbage collection (GC) to the host, thereby providing more stable performance, increased capacity, and extended device lifespan. However, the sequential write constraint poses some problems for file system design on ZNS devices, particularly leading to bottlenecks in multi-threaded performance for concurrent write requests. Through comprehensive experiments, we analyze the scalability issues of existing POSIX file systems on NVMe ZNS SSDs and identify the root causes: (1) current file systems generally fail to simultaneously utilize the throughput of multiple zones, and (2) their methods for concurrent writing within a single zone are inefficient. To fully exploit the concurrent performance of ZNS SSDs, we propose SchInFS, a novel multi-head logging ZNS SSD file system that integrates the functions of the block I/O scheduler. Firstly, SchInFS employs a multi-head logging design to leverage the throughput of multiple zones concurrently. Secondly, it provides an independent merge queue for each log, facilitating efficient cross-thread write blocks. Finally, SchInFS uses a Block I/O Submission Controller (BSC) to ensure the timely submission of requests and ordered writing within a single zone. Evaluation on real devices demonstrates the effectiveness of SchInFS, showcasing a substantial improvement in the concurrency performance of ZNS SSDs by up to 71.94% compared to current ZNS SSD file systems.

As society progresses, liquid identification plays an increasingly important role in human life. But for now, minority of existing liquid identification solutions on the market can meet daily requirements of being ubiquitous, cost-effective and non-intrusive enough. In this work, we propose ASLiquid, the first liquid counterfeit identification system with commercial off-the-shelf earphones. Our core insight is that earphones can effectively induce acoustic resonance in container, and this phenomenon is observed highly associated with the changes in liquid density and solute compositions. Deploying ASLiquid introduces three main challenges: hardware heterogeneity among different earphones, diversity of user operations, and data complexity due to variations in liquid volume and device placement. To address these issues, we first propose to eliminate the existence of hardware noise and frequency response diversity for an earphone-irrelevant solution. Afterwards, we design a user operation adaptation algorithm to extract valuable feature data during each measurement period. To alleviate problems in data complexity, we propose a spectrum projection algorithm that can effectively generate CFR data of unknown liquid volumes and a VAE based anomaly detection model for counterfeit identification. We evaluate our system with six different earphones and under various conditions. Experimental results reveal that ASLiquid can achieve F1 scores of 95%-99.25% for seven frequently occurring liquid counterfeit tasks, even in specialized attacks on liquids with 1% difference in mass fraction and different types of solutions but with the same density.

With the improvement in quality of life, ensuring liquid safety has become increasingly important, leading to a growing focus on liquid identification technologies. While extensive prior work has employed RF signals for this purpose, such methods typically require expensive and bulky signal transmitters or receivers. In this work, we explore a novel modality for liquid identification, i.e. container acoustic resonance. Our key observation is that the acoustic resonance spectrum of each liquid-container system is strongly correlated with the liquid's density and solute composition. We design and implement a simple prototype for liquid identification with low-cost acoustic sensors. The spectrum of channel frequency response for each liquid-container system is extracted as the criterion. The results show that the average accuracy for classifying 10 common liquids is 99.4% with ResNet-5.

Hybrid memory systems show strong potential to satisfy the growing memory demands of modern applications by combining different memory technologies. Due to the different performance characteristics of hybrid memories, a data migration strategy that migrates hot data to a faster memory is critical to the overall performance. Prior works have focused on identifying hot data and migration decisions. However, we find that the fix-sized global migration granularity in existing data migration schemes results in suboptimal performance on most workloads. The key observation is that the optimal migration granularity varies with access patterns. This paper proposes AGDM, an access-pattern-aware Adaptive Granularity Data Migration strategy for hybrid memory systems. AGDM tracks memory access patterns in runtime and accordingly adopts the most appropriate migration mode and granularity. The novel remapping-migration decoupled metadata organization enables AGDM to set local optimal gran-ularities for memory regions with different access patterns. Our evaluation shows that, compared to the state-of-the-art scheme, AGDM gets an average performance improvement of 20.06% with 29.98% energy savings.

Tree index structures are widely employed in modern storage systems to support high-performance queries. Persistent memory (PM) brings a new opportunity and challenge for tree indexes. Among persistent tree indexes, we find the radix tree is more suitable than B-Tree for the byte-ability of PM. However, the hierarchy of radix remains excessively high, resulting in high read latency. Node splitting imposes a significant overhead on PM. To address these challenges, we propose RWORT, a read and write optimized radix tree for PM. The key focus of RWORT is to minimize random access in PM and provide efficient write operations. RWORT proposed a hierarchical compression mechanism to significantly reduce the tree height. Additionally, RWORT incorporates mini bloom filters to reduce unnecessary access on PM. For efficient write operations, RWORT uses the lazy split flag and the double-linked pointers to reduce the critical path delay. Furthermore, RWORT introduces a low-overhead ring-based bit tree allocator that improves allocation efficiency on PM. Our experiments show that RWORT improves up to 1.62x/4.91x respectively compared to the state-of-the-art radix tree/B-Tree. RWORT also exhibits higher performance in real-world storage systems such as Memcached.

Hybrid storage systems can fully leverage the advantages of multiple devices to achieve better performance. However, current systems are designed primarily for a slow disk with an expensive fast device at high costs. They ignore device features and workload status while placing data. The issue of cache pollution is affecting their data hotness identification. Besides, inconsistent load status in multiple devices is overlooked during migration. These shortcomings constrain the overall performance of the system.To solve this, we propose HyF2FS, a hybrid storage filesystem based on F2FS. HyF2FS features a cache-tiering integrated architecture that stores hot data and metadata in an accelerator while asynchronously migrating cold data to the SSD, which provides cost-effective opportunities to optimize device parallelism. HyF2FS uses multidimensional scores to place data on the appropriate device to achieve high bandwidth. To improve data hotness identification, HyF2FS proposes two-level counters. Besides, a migration window is employed to minimize the impact of migration on foreground I/O. By implementing these scheduling algorithms, HyF2FS can fully exploit the parallelism of both fast and slow devices. Experimental results demonstrate significant improvements in throughput (116%-244%) and latency reduction (49%-64%) compared to F2FS and other hybrid storage systems.

As cloud platforms become increasingly popular, accurately understanding I/O behaviors in modern cloud storage is of paramount importance for system design and optimization. This paper sheds new light on the correlation of inter-arrival times of both read and write requests at the block level in four representative cloud storage workloads – AliCloud, Systor’17, MSRC and FIU. Our study reveals that I/O arrivals at the block level are very complex in modern cloud storage. There is a certain degree of correlation in the long-term timescale for request arrival intervals in AliCloud and Systor’17_read. Request arrival intervals in MSRC, FIU and Systor’17_write, however, are almost uncorrelated. The Gaussianity test confirms that I/O burstiness appears to be Gaussian in AliCloud_write and Systor’17_read, but the burstiness is non-Gaussian in other workloads. Importantly, we unfold the existence of self-similarity in cloud storage workloads with a certain degree of correlations, via visual evidence, the autocorrelation structure of the aggregated process of I/O request sequences, and Hurst parameter estimates. We further design an alpha-stable workload model for synthetic I/O generation, and the experimental results demonstrate that our model has an edge over conventional models in terms of accurately emulating I/O burstiness.

Emerging persistent memory (PM) has the potential to substitute DRAM due to its near-DRAM performance and durability similar to disks. However, hash tables designed for DRAM cannot be directly adopted for PM. Moreover, prior studies on hash tables using Optane DC PM modules (DCPMMs) have shown suboptimal scalability and write performance due to expensive lock-based concurrent control and massive data movement caused by expansion. In this article, we propose an opportunistic lock-free parallel multisplit extendible hashing scheme (PMEH). First, PMEH achieves lock-free operations for evenly distributed data by partitioning the hash table into multiple zones and assigning each zone to one thread. Second, PMEH employs an opportunistic lock-free parallel scheme to effectively handle skewed data distribution, which maximizes the utilization of lock-free operations by enabling dynamic switching between lock-free and locking operations. Finally, PMEH uses multisplit with gradual splitting, instead of 2-split, to reduce the frequency of hash table expansion and, hence, reduce the data movement during expansion. The experimental results under the widely used YCSB workloads demonstrate that PMEH achieves excellent scalability regardless of data distribution. Moreover, PMEH significantly speeds up insertions by$1.44\times $–$15.4\times $, and deletion by$2.04\times $–$18.07\times $compared to other state-of-the-art hashing schemes. In addition, PMEH reduces at least 52% of extra writes while providing instant recovery.

Modern computing systems and data-intensive applications are eager for larger and faster memory. Building hybrid memory systems with different memory technologies has become a dominant trend to satisfy these demands. For hybrid memory systems, page migration schemes that dynamically migrate frequently accessed hot pages into faster memory are crucial for improving performance. However, existing migration schemes are either too aggressive, resulting in unnecessary extra traffic, or too conservative to quickly adapt to changes in access patterns. Besides, the extra latency introduced by querying metadata is often ignored or handled in an unscalable manner. In this article, we propose a relative hotness page migration (RHPM) strategy, which discovers hot pages in a set of pages by competing with each other rather than comparing with a threshold. The migration is performed only when a new page wins the competition. To overlap latency due to access metadata, RHPM fetches metadata and data in parallel. In addition, it enables a small metadata buffer to speed up metadata access. Compared to the state-of-the-art scheme, RHPM requires only 1/512 of the on-chip capacity, significantly reducing on-chip hardware overhead. Evaluation of RHPM with simulations of 25 workloads shows that RHPM outperforms the state-of-the-art scheme by an average of 13.34% in performance and saves 44.19% on energy, demonstrating better resilience to changes in access patterns.

Emerging Non-volatile Memory (NVM) is attractive because of its byte-addressability, durability, and DRAM-scale latency. Hashing indexes have been extensively used to provide fast query services in the storage system. Recent research proposes crash-consistent and write-optimized hashing indexes for NVM. However, existing NVM-based hashing indexes suffer from limited scalability when running on a Commercial Non-Volatile Memory product, named Intel Optane DC Persistent Memory Module (DCPMM), due to the limited bandwidth of Optane DCPMM. To achieve a high load factor, existing NVM-based hashing indexes often evict an existing item to its alternative position, which incurs extra write and will consume the limited bandwidth. Moreover, the lock operations and metadata updates further saturate the limited bandwidth and prevent the hash table from scaling. In order to achieve scalability performance as well as a high load factor for the NVM-based hashing index, we design a new persistent hashing index, called REH, based on extendible hashing. REH (1) proposes a selective persistence scheme that stores buckets in NVM and places directory and metadata in DRAM to reduce both unnecessary NVM reads and writes, (2) uses 256B sized-buckets, as 256B is the internal data access size in Optane DCPMM, and the buckets are directly pointed to by directory entries, (3) leverages fingerprinting to further reduce unnecessary NVM reads, (4) employs failure-atomic bucket split to reduce bucket split overhead. Evaluations show that REH outperforms the state-of-the-art NVM-based hashing indexes by up to 1.68~7.78×. In the meantime, REH can achieve a high load factor.

Intel Optane DC Persistent Memory Module (DCPMM) is the first commercially available non-volatile memory (NVM) product and can be directly placed on the processor’s memory bus along with DRAM to serve as a hybrid memory. Compared with DRAM, NVM has 3× read latency and similar write latency, while the read and write bandwidths of NVM are only 1/3rdand 1/6thof those of DRAM. However, existing hashing schemes fail to reap those performance characteristics. We propose HASDH, a hotspot-aware and scalable dynamic hashing built on the hybrid DRAM-NVM memory. HASDH maintains structure metadata (i.e., directory) in DRAM and persists key-value items in NVM. To reduce hot key-value items’ access cost, HASDH caches frequently-accessed key-value items in DRAM with a dedicated caching strategy. To achieve scalable performance for multicore machines, HASDH maintains locks in DRAM that avoid the extra NVM read-write bandwidth consumption caused by lock operations. Furthermore, HASDH chains all NVM segments using sibling pointers to the right neighbors to ensure crash consistency and leverages log-free NVM segment split to reduce logging overhead. On an 18-core machine with Intel Optane DCPMM, experimental results show that HASDH achieves 1.43∼7.39× speedup for insertions, 2.08~9.63× speedup for searches, and 1.78~3.01× speedup for deletions, compared with start-of-the-art NVM-based hashing indexes.

Emerging persistent memory (PM) is a promising technology that provides near-DRAM performance and disk-like durability. However, many data structures designed based on DRAM, such as hash table and B-tree, are sub-optimal for PM. Prior studies have shown that the scalability of hash tables on Intel Optane DC Persistent Memory Modules (DCPMM) degrades significantly due to expensive lock-based concurrency control and massive data movements during rehashing. This paper proposes a lock-free parallel multi-split extendible hashing scheme (PMEH), which eliminates the lock contention overhead, reduces data movements during rehashing, and ensures data consistency. Under the widely used YCSB workloads, the evaluation results show that compared to other state-of-the-art hashing schemes, PMEH is up to 1.38x faster for insertion and up to 1.9x faster for deletion, while reducing 52% extra writes. In addition, PMEH can ensure instant recovery regardless of data size.

Modern file systems employ journaling techniques to guarantee data consistency in case of unexpected system crashes or power failures. However, journaling file systems usually suffer from performance decrease due to the extra journal writes. Moreover, the emerging non-volatile memory technologies (NVMs) have the potential capability to reduce the journaling overhead by being deployed as the journaling storage devices. However, traditional journaling techniques, which are designed for hard disks, fail to perform efficiently in NVMs. In order to address this problem, we propose an NVM-based journaling scheme, called NJS. The basic idea behind NJS is to reduce the journaling overhead of traditional file systems while fully exploiting the byte-accessibility characteristic of NVM, and alleviating the slow write and endurance limitation of NVM. Our NJS consists of four major contributions: (1) In order to decrease the amount of journal writes, NJS only needs to write the file system metadata and over-write data to NVM as write-ahead logging, thus alleviating the slow write and endurance limitation of NVM. (2) NJS adopts a wear aware strategy for NVM journal block allocation in which each block can be evenly worn out, thus further extending the lifetime of NVM. (3) We propose a novel journaling update scheme in which journal data blocks can be updated in the byte-granularity based on the difference of the old and new versions of journal blocks, thus fully exploiting the unique byte-accessibility characteristic of NVM. (4) NJS includes a garbage collection mechanism that absorbs the redundant journal updates, and actively delays the checkpointing to the file system. Evaluation results show the efficiency and efficacy of NJS. For example, compared with Ext4 with a ramdisk-based journaling device, the throughput improvement of Ext4 with our NJS is up to 131.4 percent.

The development of non-volatile memory technologies (NVMs) has attracted interest in designing data structures that are efficiently adapted to NVMs. In this context, several NVM-friendly hashing schemes have been proposed to reduce extra writes to NVMs, which have asymmetric properties of reads and writes and limited write endurance compared with traditional DRAM. However, these works neither consider the cost of cacheline flush and memory fence nor provide mechanisms to maintain data consistency in case of unexpected system failures. In this paper, we propose a write-efficient and consistent hashing scheme, called group hashing. The basic idea behind group hashing is to reduce the consistency cost while guaranteeing data consistency in case of unexpected system failures. Our group hashing consists of two major contributions: (1) We use 8-byte failure-atomic write to guarantee the data consistency, which eliminates the duplicate copy writes to NVMs, thus reducing the consistency cost of the hash table structure. (2) In order to improve CPU cache efficiency, our group hashing leverages a novel technique, i.e., group sharing, which divides the hash table into groups and deploys a contiguous memory space in each group to deal with hash collisions, thus reducing CPU cache misses to obtain higher performance in terms of request latency. We have implemented group hashing and evaluated the performance by using three real-world traces. Extensive experimental results demonstrate that our group hashing achieves low request latency as well as high CPU cache efficiency, compared with state-of-the-art NVM-based hashing schemes.

Modern file systems employ journaling techniques to guarantee data consistency in case of unexpected system crashes or power failures. However, journaling file systems usually suffer from performance decrease due to the extra journal writes. Moreover, the emerging non-volatile memory technologies (NVMs) have the potential capability to improve the performance of journaling file systems by being deployed as the journaling storage devices. However, traditional journaling techniques, which are designed for hard disks, fail to perform efficiently in NVMs. In order to address this problem, we propose an NVM-based journaling scheme, called NJS. The basic idea behind NJS is to reduce the journaling overhead of traditional file systems while fully exploiting the byte-accessibility characteristic, and alleviating the relatively slow write and endurance limitation of NVM. Our NJS consists of three major contributions: (i) In order to minimize the amount of journal writes, NJS only needs to write the metadata of file systems and over-write data to NVM as write-ahead logging, thus alleviating the relatively slow write and endurance limitation of NVM. (ii) We propose a novel journaling update scheme in which the journaling data blocks can be updated in the byte-granularity based on the difference of the old and new versions of journal blocks, thus fully exploiting the unique byte-accessibility characteristic of NVM. (iii) NJS includes a garbage collection mechanism that absorbs the redundant journal updates, and actively delays the checkpointing to the file system. Evaluation results show the efficiency and efficacy of NJS. For example, compared with original Ext4 with a ramdisk-based journaling device, the throughput improvement of Ext4 with our NJS is up to 137.1%.

With the development of technology and price decline, flash-based Solid state drives (SSDs) are rapidly used to construct RAIDs by storage vendors. SSD does not need to seek and rotate, therefore, its read performance is much better than that of HDD. However, the small write performance of SSD is limited by its inherent characteristics such as out- of-place updates and garbage collection. The traditional parity-based RAID also has small write problem because of parity updating. SSD-based RAID, which is called RAIS, is generally based on the traditional RAID design and implementation. Consequently, handling small write requests is a serious challenge when SSD is used to construct parity-based RAID. In RAIS storage system, small write requests not only result in poor performance, but also shorten the lifetime of each SSD. In this paper, we propose a novel write through cache method, called CRAIS5, which uses a RAM as the write cache of RAIS5, and adopts the write-through mode to delay the parity update. The write-through cache method makes full use of the flash characteristics, and removes the pre-read operation. CRAIS5 improves the small write performance and reduces the erase time. We have implemented the CRAIS5 prototype in Disksim simulator, and used the real traces to evaluate the performance. The evaluations demonstrate that our CRAIS5 outperforms RAIS5, and PPC, on average, by 42.82%, and 34.49% respectively.

In modern RAID-structured storage systems, reliability is guaranteed by the use of parity blocks. But the parity-update overheads upon each write request have become a performance bottleneck of RAID systems. In some ways, an attached log disk is used to improve the write performance by delaying the parity blocks update. However, these methods are data-block-oriented and they need more time to rebuild or synchronize the RAID system when a data disk or the log disk fails. In this paper, we propose a novel optimization method, called SWO, which can improve RAID write performance and reconstruction performance. Moreover, when handling a write request, the SWO chooses reconstruction- write or read-modify-write combining with the log information to further minimize the number of pre- read data blocks. We have implemented the proposed SWO prototype and carried out some performance measurements using IOmeter and RAIDmeter. We have implemented the main idea of RAID6L in RAID5 and call it RAID5L. At the same time, we have evaluated the reconstruction time and the synchronization time of the SWO. Our experiments demonstrate that the SWO significantly improves write performance and saves more time than Data Logging and RAID5L when rebuilding and synchronizing.

For years, the increasing popularity of flash memory has been changing storage systems. Flash-based solid-state drives (SSDs) are widely used as a new cache tier on top of hard disk drives (HDDs) to speed up data-intensive applications. However, the endurance problem of flash memory remains a concern and is getting worse with the adoption of MLC and TLC flash. In this article, we propose a novel cache management algorithm for flash-based disk cache named Lazy Adaptive Replacement Cache (LARC). LARC adopts the idea of selective caching to filter out seldom accessed blocks and prevent them from entering cache. This avoids cache pollution and preserves popular blocks in cache for a longer period of time, leading to a higher hit rate. Meanwhile, by avoiding unnecessary cache replacements, LARC reduces the volume of data written to the SSD and yields an SSD-friendly access pattern. In this way, LARC improves the performance and endurance of the SSD at the same time. LARC is self-tuning and incurs little overhead. It has been extensively evaluated by both trace-driven simulations and synthetic benchmarks on a prototype implementation. Our experiments show that LARC outperforms state-of-art algorithms for different kinds of workloads and extends SSD lifetime by up to 15.7 times.

Software delays have become the bottleneck to the overall storage system. Particularly, in iSCSI-based storage system, more disks in the target-end system are configured to make up a target node, but requests initiated by the initiator-end system will go through the traditional single I/O path before they are re-requested by the target-end system, which cannot exploit the serviceability of the target multi-disks adequately. This paper introduces the metadata-request queue to handle metadata I/O requests and scheduling differs from the request queue that exists in the initiator-end system of iSCSI-based storage system, given the different characteristics of metadata and data I/O requests. Instead of dispatching all requests exclusively to the single queue and unified scheduling, a new metadata-request queue is introduced for every logical device and is implemented with immediate response mode to eliminate waiting delays, considering the characteristics of small size, high frequency and scattered accesses that require for real-time response. Data requests in the data-request queue are delivered to the scheduling queue in an I/O waiting state, which is known as plugged-unplugged mechanism. Evaluation of our system using Postmarks and IOMeter shows 9%-19% I/O performance improvement.

NAND flash memory has attracted wide attention in both academia and industry in recent years. Its high random access performance fills the gap between DRAM and hard disks. While MLC is endorsed for higher density and lower cost per bit, it suffers from poor performance and endurance. Dual-mode flash combines SLC and MLC in a single device and thus provides the opportunity to trade density for performance. In this paper, we propose the Scalable Flash Storage(SFS) abstraction layer to facilitate cache management on dual-mode flash. SFS exposes a virtualized address space to hide the variable density of the medium. A differentiated write interface is introduced, which allows the cache manager to explicitly send write requests to SLC for high performance. SFS dynamically scales the proportions of SLC and MLC to balance between cache capacity and performance. SFS provides partially persistent storage service. It allows the cache manager to manage the data persistence on flash so that critical data can be retained persistently. Non-persistent data are discarded during garbage collection to mitigate write amplification. Based on the SFS, a Dual-mode Flash Cache(DMFC) architecture is designed to utilize the configurable density and performance. Experimental results show that DMFC can significantly improve overall performance for various workloads.

Flash-based Solid State Drive (SSD) has limitations in terms of cost and lifetime. It is used as a second-level cache between main memory and traditional HDD-based storage widely. Adopting traditional cache algorithms, which are designed primarily depending on temporal locality and popular blocks, to SSD-based second-level disk cache can cause unnecessary cache replacements, which not only degrade the cache performance but also shorten the lifetime of SSD. To overcome this problem, this paper proposes a performance-effective Regional Popularity-Aware Cache replacement algorithm (RPAC). Instead of a single block, the popularity of a region which is constituted by many adjacent disk blocks is recorded and used to determine replacing a block or not. In this way, the spatial locality of disk access is completely leveraged and sequential I/O blocks are gathered in SSD cache. Furthermore, it reduces the number of unnecessary cache replacement and erasure operation on SSD, prolonging its lifetime. We have implemented RPAC in real system and evaluated it by many workloads. Compared to traditional cache algorithms, it improve I/O throughput by up to 53% and reduce cache replacements of SSD up to 98.5%.

File system performance is dominated by small and frequent metadata access. Metadata is stored as blocks on the hard disk drive. Partial metadata update results in whole-block read or write, which significantly amplifies disk I/O. Furthermore, a huge performance gap between the CPU and disk aggravates this problem. In this article, a file system metadata accelerator (referred to as FSMAC) is proposed to optimize metadata access by efficiently exploiting the persistency and byte-addressability of Nonvolatile Memory (NVM). The FSMAC decouples data and metadata access path, putting data on disk and metadata in byte-addressable NVM at runtime. Thus, data is accessed in a block from I/O the bus and metadata is accessed in a byte-addressable manner from the memory bus. Metadata access is significantly accelerated and metadata I/O is eliminated because metadata in NVM is no longer flushed back to the disk periodically. A lightweight consistency mechanism combining fine-grained versioning and transaction is introduced in the FSMAC. The FSMAC is implemented on a real NVDIMM platform and intensively evaluated under different workloads. Evaluation results show that the FSMAC accelerates the file system up to 49.2 times for synchronized I/O and 7.22 times for asynchronized I/O. Moreover, it can achieve significant performance speedup in network storage and database environment, especially for metadata-intensive or write-dominated workloads.

File system performance is dominated by metadata access because it is small and popular. Metadata is stored as block in the file system. Partial metadata update results in whole block read and write which amplifies disk I/O. Huge performance gap between CPU and disk aggravates this problem. In this paper, a file system metadata accelerator (referred as FSMAC) is proposed to optimize metadata access by efficiently exploiting the advantages of Nonvolatile Memory (NVM). FSMAC decouples data and metadata I/O path, putting data on disk and metadata on NVM at runtime. Thus, data is accessed in block from I/O bus and metadata is accessed in byte-addressable manner from memory bus. Metadata access is significantly accelerated and metadata I/O is eliminated because metadata in NVM is not flushed back to disk periodically anymore. A light-weight consistency mechanism combining fine-grained versioning and transaction is introduced in the FSMAC. The FSMAC is implemented on the basis of Linux Ext4 file system and intensively evaluated under different workloads. Evaluation results show that the FSMAC accelerates file system up to 49.2 times for synchronized I/O and 7.22 times for asynchronized I/O.

The increasing popularity of flash memory has changed storage systems. Flash-based solid state drive(SSD) is now widely deployed as cache for magnetic hard disk drives(HDD) to speed up data intensive applications. However, existing cache algorithms focus exclusively on performance improvements and ignore the write endurance of SSD. In this paper, we proposed a novel cache management algorithm for flash-based disk cache, named Lazy Adaptive Replacement Cache(LARC). LARC can filter out seldom accessed blocks and prevent them from entering cache. This avoids cache pollution and keeps popular blocks in cache for a longer period of time, leading to higher hit rate. Meanwhile, LARC reduces the amount of cache replacements thus incurs less write traffics to SSD, especially for read dominant workloads. In this way, LARC improves performance and extends SSD lifetime at the same time. LARC is self-tuning and low overhead. It has been extensively evaluated by both trace-driven simulations and a prototype implementation in flashcache. Our experiments show that LARC outperforms state-of-art algorithms and reduces write traffics to SSD by up to 94.5% for read dominant workloads, 11.2-40.8% for write dominant workloads.

Both physical disk failure and logical errors such as software error, user abuse and virus attacks may cause data lose. The risk of logical errors is far greater than physical disk failure. Moreover, existing RAID solution cannot satisfy the reliability requirement in face of the logical errors in data centers. It is therefore becoming increasingly important for RAID-based storage systems to be able to recover data to any point-in-time when logical errors occur. We proposed a novel storage array architecture, Herp Rap, which is able to recover data from both physical disk failure and logical errors. We have implemented a prototype of Herp Rap and carried out extensive performance measurements using DBT-2 and file system benchmarks. Our experiments demonstrated that the proposed Herp Rap is able to track or recover data to any point-in-time quickly by tracing back the history of block logs. Moreover, Herp Rap outperforms existing HDD-based or SSD-based RAID5 with copy-on-write (COW) snapshot in terms of performance, energy efficiency, failure recovery ability and reliability.

The RAID6 provides high reliability using double-parity-update at cost of high write penalty. In this paper, we propose HRAID6ML, a new logging architecture for RAID6 systems for enhanced energy efficiency, performance and reliability. HRAID6ML explores a group of Solid State Drives (SSDs) and Hard Disk Drives (HDDs): Two HDDs (parity disks) and several SSDs form RAID6. The free space of the two parity disks is used as mirrored log region of the whole system to absorb writes. The mirrored logging policy helps to recover system from parity disk failure. Mirrored logging operation does not introduce noticeable performance overhead to the whole system. HRAID6ML eliminates the additional hardware and energy costs, potential single point of failure and performance bottleneck. Furthermore, HRAID6ML prolongs the lifecycle of the SSDs and improves the systems energy efficiency by reducing the SSDs write frequency. We have implemented proposed HRAID6ML. Extensive trace-driven evaluations demonstrate the advantages of the HRAID6ML system over both traditional SSD-based RAID6 system and HDD-based RAID6 system.

Flash-based Solid State Drive (SSD) has been productively shipped and deployed in large scale storage systems. However, a single flash-based SSD cannot satisfy the capacity, performance and reliability requirements of the modern storage systems that support increasingly demanding data-intensive computing applications. Applying RAID schemes to SSDs to meet these requirements, while a logical and viable solution, faces many challenges. In this article, we propose a Hybrid Parity-based Disk Array architecture (short for HPDA), which combines a group of SSDs and two hard disk drives (HDDs) to improve the performance and reliability of SSD-based storage systems. In HPDA, the SSDs (data disks) and part of one HDD (parity disk) compose a RAID4 disk array. Meanwhile, a second HDD and the free space of the parity disk are mirrored to form a RAID1-style write buffer that temporarily absorbs the small write requests and acts as a surrogate set during recovery when a disk fails. The write data is reclaimed to the data disks during the lightly loaded or idle periods of the system. Reliability analysis shows that the reliability of HPDA, in terms of MTTDL (Mean Time To Data Loss), is better than that of either pure HDD-based or SSD-based disk array. Our prototype implementation of HPDA and the performance evaluations show that HPDA significantly outperforms either HDD-based or SSD-based disk array.

A single flash-based Solid State Drive (SSD) can not satisfy the capacity, performance and reliability requirements of a modern storage system supporting increasingly demanding data-intensive computing applications. Applying RAID schemes to SSDs to meet these requirements, while a logical and viable solution, faces many challenges. In this paper, we propose a Hybrid Parity-based Disk Array architecture, HPDA, which combines a group of SSDs and two hard disk drives (HDDs) to improve the performance and reliability of SSD-based storage systems. In HPDA, the SSDs (data disks) and part of one HDD (parity disk) compose a RAID4 disk array. Meanwhile, a second HDD and the free space of the parity disk are mirrored to form a RAID1-style write buffer that temporarily absorbs the small write requests and acts as a surrogate set during recovery when a disk fails. The write data is reclaimed back to the data disks during the lightly loaded or idle periods of the system. Reliability analysis shows that the reliability of HPDA, in terms of MTTDL (Mean Time To Data Loss), is better than that of either pure HDD-based or SSD-based disk array. Our prototype implementation of HPDA and performance evaluations show that HPDA significantly outperforms either HDD-based or SSD-based disk array.

This paper proposes a simple and practical RAID reconstruction optimization scheme, called JOurnal-guided Reconstruction (JOR). JOR exploits the fact that significant portions of data blocks in typical disk arrays are unused. JOR monitors the storage space utilization status at the block level to guide the reconstruction process so that only failed data on the used stripes is recovered to the spare disk. In JOR, data consistency is ensured by the requirement that all blocks in a disk array be initialized to zero (written with value zero) during synchronization while all blocks in the spare disk also be initialized to zero in the background. JOR can be easily incorporated into any existing reconstruction approach to optimize it, because the former is independent of and orthogonal to the latter. Experimental results obtained from our JOR prototype implementation demonstrate that JOR reduces reconstruction times of two state-of-the-art reconstruction schemes by an amount that is approximately proportional to the percentage of unused storage space while ensuring data consistency.

ISCSI has been paid much attention since it allows the storage data to be transported over the popular TCP/IP networks and takes advantage of the networks. Carrying data over the TCP/IP networks introduces the security problem. The IP security mechanisms, such as IPSec, always degrade the performance greatly. In this paper we introduced a parallel encryption method, combined with the IPSec A,H to provide the security and high performance for the iSCSI storage system. We have implemented parallel encryption with IPSec AH in the UNH iSCSI software. Numerical results using popular benchmark program and OLTP traces have shown dramatic performance gain and the average response time reduced greatly.

High performance, high bandwidth, and scalable network storage system is the requirement tendency of most data-intensive applications. Distributed RAID, as a storage architecture, is widely used in cluster and distributed computing environments. But the data placement is mechanical and penalty for maintaining consistency is suffered. In this paper we propose a novel block-level storage architecture called iVISA (iSCSI based virtual interface storage architecture) which employs the concepts both distributed RAID and virtualization storage. All storage resources distribute in a high performance VI network and are managed by a metadata server. A number of iSCSI target nodes act as the entrances of iVISA system for accessing through iSCSI connections. The storage resources are dynamically allocated to users during I/O accessing. The users' logical block addresses are intelligently and flexibly mapped to the physical block addresses based on the current workload of storage nodes and data layout. The evaluation result shows that better performance can be achieved by allocation and mapping dynamically and flexibly with optimized allocation strategy. A hybrid strategy considering both data layout and load of storage nodes has a 30% higher I/O performance than conventional distributed RAID.