Solid State Drives Research Articles

An Overview of the Future of Memory Cells: Advances in SSD Technology

This paper explores the topic of Solid-State Drives (SSDs) and NAND flash memory. Information of this paper is mainly obtained from authoritative academic websites and papers related to SSDs and flash memory. Memory cells are fundamental components in various electronic devices. In the field of data storage, SSDs represent the latest generation of storage devices, offering significantly better performance and functionality than traditional hard disk drives (HDDs). NAND flash memory chips are crucial component of SSDs, particularly 3D NAND, which exhibits exceptional performance and holds significant potential for future advancements. This paper will commence by providing an overview of SSDs and HDDs, followed by a comparison between NAND flash and NOR flash, and ultimately analysing the future of NAND flash memory technology employed in SSDs, with a focus on elucidating the advantages of 2D NAND over 3D NAND. Additionally, this paper explains a few challenges that NAND technology faces currently, as well as some possible solutions.

Improving Cloud Database Performance Using Storage Technologies

Cloud databases have become indispensable for modern applications due to their scalability, flexibility, and cost-effectiveness. However, optimizing performance in cloud environments can be challenging due to factors such as network latency, data distribution, and resource contention. This paper explores various storage technologies and techniques that can significantly enhance cloud database performance. We delve into the characteristics of different storage types, including solid-state drives (SSDs), hard disk drives (HDDs), and object storage, and discuss their suitability for various workloads. Additionally, we examine caching mechanisms, data compression, and deduplication strategies that can optimize data access and reduce storage costs. Furthermore, we explore the impact of database architecture choices, such as distributed databases and inmemory databases, on performance. We also discuss the importance of effective query optimization and indexing techniques. By understanding and implementing these storage technologies and techniques, organizations can achieve substantial improvements in cloud database performance, leading to better user experiences and increased application efficiency

Advancements and Best Practices in Hard Disk Drives (HDDs) Repair: A Comprehensive Review of Diagnostic Techniques, Repair Methods, and Preventive Strategies

Hard Disk Drives (HDDs) remain a crucial component of data storage systems despite the advent of Solid State Drives (SSDs). However, the mechanical and electronic nature of HDDs makes them susceptible to various failures over time. This article provides a comprehensive examination of HDD repair, focusing on the anatomy and function of these devices, common causes of failure, diagnostic techniques, and effective repair methods. Mechanical failures, such as head crashes and spindle motor issues, electronic failures like PCB damage, and logical failures including file system corruption and bad sectors are explored in detail. Diagnostic approaches utilizing visual inspection, diagnostic software tools, hardware-based diagnostics, and SMART monitoring are discussed to aid in identifying issues accurately. Repair methods covered include data recovery techniques, mechanical repairs, electronic component replacement, and firmware fixes. Real-world case studies illustrate practical challenges and solutions in HDD repair, highlighting the complexities involved. Additionally, best practices and safety measures for handling and maintaining HDDs are provided to prevent future failures and ensure data integrity. This article aims to equip data recovery professionals, IT technicians, and tech enthusiasts with the knowledge and skills necessary to diagnose and repair HDD issues effectively. By integrating technical insights and practical approaches, it serves as a valuable resource for enhancing the reliability and longevity of HDDs in various applications.

Optimizing Garbage Collection for ZNS SSDs via In-storage Data Migration and Address Remapping

The NVMe Zoned Namespace (ZNS) is a high-performance interface for flash-based solid-state drives (SSDs), which divides the logical address space into fixed-size and sequential-write zones. Meanwhile, ZNS SSDs eliminate in-device garbage collection (GC) by shifting the responsibility of GC to the host. However, the host-side GC of ZNS SSDs is not efficient. On the one hand, data migration during GC first moves data to the host buffer and then writes back the transferred data to the new location in the SSD, resulting in an unnecessary end-to-end transfer overhead. On the other hand, due to the pre-configured mapping between zones and blocks, GC incurs a large block-to-block rewrite overhead, i.e., even if most of the data in a block of the victim zone is valid, the valid data will still be rewritten to another block in the target zone. To address these issues, this paper proposes a novel ZNS SSD design that features dynamic zone mapping, termed Brick-ZNS . Brick-ZNS implements two key functionalities: in-storage data migration and address remapping. New ZNS commands are first designed to realize in-storage data migration to avoid the end-to-end transfer overhead of GC while ensuring performance predictability. Then, a remapping strategy exploiting parallel physical blocks is proposed to reduce the large block-to-block rewrite overhead while ensuring zone-level access parallelism. The basic idea of the strategy is to directly remap the parallel physical blocks with a sufficient amount of valid data in the victim zone to the target zone, hence avoiding the large block-to-block rewrite overhead. Based on a full-stack SSD emulator, the evaluation results show that Brick-ZNS improves write throughput by 25% and SSD lifetime by 1.41 ×.

Efficient Wear-Leveling-Aware Data Placement for LSM-Tree based key-value store on ZNS SSDs

Emerging Zoned Namespace (ZNS) is a new-style Solid State Drive (SSD) that manages data in a zoned manner, which can achieve higher performance by strictly obeying the sequential write mode in each zone and alleviating the redundant overhead of garbage collections. Unfortunately, flash memory usually has a serious problem with limited program/erase cycles. Meanwhile, inappropriate data placement strategy of storage systems can lead to imbalanced wear among zones, severely reducing the lifespan of ZNS SSDs. In this paper, we propose a Wear-Leveling-Aware Data Placement (WADP) to solve this problem with negligible performance cost. First, WADP employs a wear-aware empty zone allocation algorithm to quantify the resets of zones and choose the less-worn zone for each allocation. Second, to prevent long-term zone occupation of infrequently written data (namely cold data), we propose a wear-leveling cold zone monitoring mechanism to identify cold zones dynamically. Finally, WADP adopts a real-time I/O pressure-aware data migration mechanism to adaptively migrate cold data for achieving wear-leveling among zones. We implement the proposed WADP in ZenFS and evaluate it with widely used workloads. Compared with state-of-the-art solutions, i.e., LIZA and FAR, the experimental results show that WADP can significantly reduce the standard deviation of zone resets while maintaining decent performance.

CAVE: Concurrency-Aware Graph Processing on SSDs

Large-scale graph analytics has become increasingly common in areas like social networks, physical sciences, transportation networks, and recommendation systems. Since many such practical graphs do not fit in main memory, graph analytics performance depends on efficiently utilizing underlying storage devices. These out-of-core graph processing systems employ sharding and sub-graph partitioning to optimize for storage while relying on efficient sequential access of traditional hard disks. However, today's storage is increasingly based on solid-state drives (SSDs) that exhibit high internal parallelism and efficient random accesses. Yet, state-of-the-art graph processing systems do not explicitly exploit those properties, resulting in subpar performance. In this paper, we develop CAVE, the first graph processing engine that optimally exploits underlying SSD-based storage by harnessing the available storage device parallelism via carefully selecting which I/Os to graph data can be issued concurrently. Thus, CAVE traverses multiple paths and processes multiple nodes and edges concurrently, achieving parallelization at a granular level. We identify two key ways to parallelize graph traversal algorithms based on the graph structure and algorithm: intra-subgraph and inter-subgraph parallelization. The first identifies subgraphs that contain vertices that can be accessed in parallel, while the latter identifies subgraphs that can be processed in their entirety in parallel. To showcase the benefit of our approach, we build within CAVE parallelized versions of five popular graph algorithms (Breadth-First Search, Depth-First Search, Weakly Connected Components, PageRank, Random Walk) that exploit the full bandwidth of the underlying device. CAVE uses a blocked file format based on adjacency lists and employs a concurrent cache pool that is essential to the parallelization of graph algorithms. By experimenting with different types of graphs on three SSD devices, we demonstrate that CAVE utilizes the available parallelism, and scales to diverse real-world graph datasets. CAVE achieves up to one order of magnitude speedup compared to the popular out-of-core systems Mosaic and GridGraph, and up to three orders of magnitude speedup in runtime compared to GraphChi.

X Meta : SSD-HDD-hybrid Optimization for Metadata Maintenance of Cloud-scale Object Storage

Object storage has been widely used in the cloud. Traditionally, the size of object metadata is much smaller than that of object data, and thus existing object storage systems (such as Ceph and Oasis) can place object data and metadata, respectively, on hard disk drives (HDDs) and solid-state drives (SSDs) to achieve high I/O performance at a low monetary cost. Currently, however, a wide range of cloud applications organize their data as large numbers of small objects of which the data size is close to (or even smaller than) the metadata size, thus greatly increasing the cost if placing all metadata on expensive SSDs. This article presents x Meta , an SSD-HDD-hybrid optimization for metadata maintenance of cloud-scale object storage. We observed that a substantial portion of the metadata of small objects is rarely accessed and thus can be stored on HDDs with little performance penalty. Therefore, x Meta first classifies the hot and cold metadata based on the frequency of metadata accesses of upper-layer applications and then adaptively stores the hot metadata on SSDs and the cold metadata on HDDs. We also propose a merging mechanism for hot metadata to further improve the efficiency of SSD storage and optimize range key query and insertion for hot metadata by designing composite keys. We have integrated the x Meta metadata service with Ceph to realize a high-performance, low-cost object store (called xCeph). The extensive evaluation shows that xCeph outperforms the original Ceph by an order of magnitude in the space requirement of SSD storage, while improving the throughput by up to 2.7×.

CCFTL: A novel continuity compressed page-level flash address mapping method for SSDs

Given the distinctive characteristics of flash-based solid-state drives (SSDs), such as out-of-place update scheme, as compared to traditional block storage devices, a flash translation layer (FTL) has been introduced to hide these features. In the FTL, there is an address translation module that implements the conversion from logical addresses to physical addresses. However, existing address mapping algorithms currently fail to fully exploit the mapping information generated by large I/O requests. First, based on this observation, we propose a novel continuity compressed page-level flash address mapping method (CCFTL). This method effectively compresses the mapping relationship between consecutive logical addresses and physical addresses, enabling the storage of more mapping information within the same mapping cache size. Next, we introduce two-level LRU linked list to mitigate the issue of compressed mapping entry splitting that arises from handling write requests. Finally, our experiments show that CCFTL reduced average response times by 52.67%, 16.81%, and 12.71% compared to DFTL, TPFTL, and MFTL, respectively. As the mapping cache size decreases from 2 MB to 1 MB, then further decreases to 256 KB, 128 KB, and eventually down to 64 KB, CCFTL experiences an average decline ratio of less than 3% in average response time, while the other three algorithms show an average decline ratio of 9.51%.

Dynamic zone redistribution for key-value stores on zoned namespaces SSDs

Recently, the zoned namespaces (ZNS) interface has been introduced as a new interface for solid-state drives (SSD), and commercial ZNS SSDs are starting to be used for LSM-tree-based KV-stores, including RocksDB, whose log-structured write characteristics well align with the intra-zone sequential write constraint of the ZNS SSDs. The host software for ZNS SSDs, including ZenFS for RocksDB, considers the lifetime of data when allocating zones to expedite zone reclamation. It also uses a lock-based synchronization mechanism to prevent concurrent writes to a zone, together with a contention avoidance policy that avoids allocating ‘locked’ zones to increase write throughput. However, this policy seriously damages the lifetime-based zone allocation strategy, leading to increased write amplification in KV-stores that support parallel compaction. In this paper, we delve into the underlying causes of this phenomenon and propose a novel zone management scheme, Dynamic Zone Redistribution (DZR), that can be effectively used for such KV-stores. DZR enables both high throughput and low write amplification by effectively addressing the root cause. Experimental results using micro- and macro-benchmarks show that DZR significantly reduces write amplification compared with ZenFS while preserving (or even increasing) write throughput.

SSD DISCORE - A SCORING METHOD FOR SSD'S DATA INTEGRITY VALIDATION USING DIFFERENT SMART ATTRIBUTES IN THE DIGITAL FORENSICS PROCESS

Solid-state drives (SSD) are replacing hard disk drives (HDD) in the majority of computer systems today. Because SSDs outperform HDDs in terms of efficiency, SSDs are now more necessary to replace HDDs. However, due to uncertain data integrity, SSDs are not forensically sound in design. The data on SSD is continuously changing as a result of Wear Leveling, TRIM and Garbage Collection, which makes data integrity verification in digital forensics challenging. The use of hash algorithms to validate data integrity is crucial in establishing the legitimacy of evidence gathered from suspect systems. In order to shed light on this matter, an experiment was carried out to gather data from an SSD in both user active and user non-active state. The data was then analyzed using the different attributes of Self-Monitoring, Analysis, and Reporting Technology (SMART) to ascertain the data integrity and by proposing a scoring method “SSD DiScore (SSD Data Integrity Score)”, which can aid in digital forensic investigation procedure.

Exploring data storage innovations: From DNA to holography

This paper delves into the evolution of data storage technologies, focusing on the solid-state drive (SSD) and hard disk drive (HDD) and their working principles. It explores the advancements in DNA as a storage medium, highlighting its exceptional data density and potential as a long-term storage solution. The paper discusses the challenges and limitations of DNA data storage, emphasizing the need for further research to overcome these hurdles. Additionally, the paper introduces holographic data storage as a novel optical storage technology and discusses its potential applications. It also touches on the decline of the optical disc industry due to the emergence of alternative storage technologies. The use of metal nanoparticles (MNPs) in optical disks is explored, emphasizing their role in high-contrast imaging and data storage. The paper concludes by highlighting the diverse methods of data storage, each with its unique advantages and drawbacks, and anticipates future improvements in electronic information processing technology, leading to faster data transmission and increased data storage capacity within the same volume of storage media.

Data Placement Using a Classifier for SLC/QLC Hybrid SSDs

In hybrid SSDs (solid-state drives) consisting of SLC (single-level cell) and QLC (quad-level cell), efficiently using the limited SLC cache space is crucial. In this paper, we present a practical data placement scheme, which determines the placement location of incoming write requests using a lightweight machine-learning model. It leverages information about I/O workload characteristics and SSD status to identify cold data that does not need to be stored in the SLC cache with high accuracy. By strategically bypassing the SLC cache for cold data, our scheme significantly reduces unnecessary data movements between the SLC and QLC regions, improving the overall efficiency of the SSD. Through simulation-based studies using real-world workloads, we demonstrate that our scheme outperforms existing approaches by up to 44%.

ISP Agent: A Generalized In-storage-processing Workload Offloading Framework by Providing Multiple Optimization Opportunities

As solid-state drives (SSDs) with sufficient computing power have recently become the dominant devices in modern computer systems, in-storage processing (ISP), which processes data within the storage without transferring it to the host memory, is being utilized in various emerging applications. The main challenge of ISP is to deliver storage data to the offloaded workload. This is difficult because of the information gap between the host and storage, the data consistency problem between the host and offloaded workloads, and SSD-specific hardware limitations. Moreover, because the offloaded workloads use internal SSD resources, host I/O performance might be degraded due to resource conflicts. Although several ISP frameworks have been proposed, existing ISP approaches that do not deeply consider the internal SSD behavior are often insufficient to support efficient ISP workload offloading with high programmability. In this article, we propose an ISP agent, a lightweight ISP workload offloading framework for SSD devices. The ISP agent provides I/O and memory interfaces that allow users to run existing function codes on SSDs without major code modifications, and separates the resources for the offloaded workloads from the existing SSD firmware to minimize interference with host I/O processing. The ISP agent also provides further optimization opportunities for the offloaded workload by considering SSD architectures. We have implemented the ISP agent on the OpenSSD Cosmos+ board and evaluated its performance using synthetic benchmarks and a real-world ISP-assisted database checkpointing application. The experimental results demonstrate that the ISP agent enhances host application performance while increasing ISP programmability, and that the optimization opportunities provided by the ISP agent can significantly improve ISP-side performance without compromising host I/O processing.

QoS-pro: A QoS-enhanced Transaction Processing Framework for Shared SSDs

Solid State Drives (SSDs) are widely used in data-intensive scenarios due to their high performance and decreasing cost. However, in shared environments, concurrent workloads can interfere with each other, leading to a violation of Quality of Service (QoS). While QoS mechanisms like fairness guarantees and latency constraints have been integrated into SSDs, existing transaction processing frameworks offer limited QoS guarantees and can significantly degrade overall performance in a shared environment. The reason is that the internal components of an SSD, originally designed to exploit parallelism, struggle to coordinate effectively when QoS mechanisms are applied to them. This article proposes a novel QoS -enhanced transaction pro cessing framework, called QoS-pro, which enhances QoS guarantees for concurrent workloads while maintaining high parallelism for SSDs. QoS-pro achieves this by redesigning transaction processing procedures to fully exploit the parallelism of shared SSDs and enhancing QoS-oriented transaction translation and scheduling with parallelism features in mind. In terms of fairness guarantees, QoS-pro outperforms state-of-the-art methods by achieving 96% fairness improvement and 64% maximum latency reduction. QoS-pro also shows almost no loss in throughput when compared with parallelism-oriented methods. Additionally, QoS-pro triggers the fewest Garbage Collection (GC) operations and minimally affects concurrently running workloads during GC operations.

In-Storage Computing for Hadoop MapReduce Framework: Challenges and Possibilities

Solid State Drives (SSDs) were initially developed as faster storage devices intended to replace conventional magnetic Hard Disk Drives (HDDs). However, high computational capabilities enable SSDs to be computing nodes, not just faster storage devices. Such capability is generally called ”In-Storage Computing (ISC)”. Today’s Hadoop MapReduce framework has become a de facto standard for big data processing. This paper explores In-Storage Computing challenges and opportunities for the Hadoop MapReduce framework. For this, we integrate a Hadoop MapReduce system with ISC SSD devices that implement the Hadoop Mapper inside real SSD firmware. This offloads Map tasks from the host MapReduce system to the ISC SSDs. We additionally optimize the host Hadoop system to make the best use of our proposed ISC Hadoop system. Experimental results demonstrate our ISC Hadoop MapReduce system achieves a remarkable performance gain (2.3 faster) as well as significant energy savings (11.5 lower) compared to a typical Hadoop MapReduce system. Further, the experiment suggests such ISC augmented systems can provide a very promising computing model in terms of a system scalability.

H[formula omitted]-RAID: Improving the reliability of SSD RAID with unified SSD and HDD hybrid architecture

With the increasing development of SSD (Solid-State Drives) technology, SSD RAID (Redundant Arrays of Independent Disks) has been widely deployed in enterprise data centers. However, the inherent write endurance issue of SSD seriously affects the reliability of the array. Meanwhile, compared with conventional HDD-based RAID, SSD RAID exhibits very different failure characteristics, such as correlated failure (Balakrishnan et al., 2010) under RAID-5. In this paper, we present a Hybrid High reliability RAID architecture, named H2-RAID, by equipping each SSD with an extra HDD as the backup to improve the reliability of SSD RAID. Considering the relatively longer write latency of HDD, in H2-RAID, we first propose an HDD-aware backup mechanism to smartly aggregate random writes into sequential writes to decrease performance degradation. In addition, to cope with the scenarios of SSD failure, an HDD-aware reconstruction method is further proposed to guarantee the reliability and the online transaction processing performance. We build a novel Markov process-based mathematical model to analyze the reliability of different architectures, and the theoretical results prove the reliability of H2-RAID is much higher than that of traditional SSD RAID. To more accurately evaluate the performance influence of HDD on H2-RAID, we develop a simulator based on Disksim and the experimental results show H2-RAID significantly increases the reliability compared with SSD array (under RAID-5) while with little performance loss on average.

Access Characteristic Guided Partition for Nand Flash-Based High-Density SSDs

NAND flash-based solid-state drives (SSDs) are a kind of widely adopted storage. However, state-of-the-art works presented that the SSD always suffers from significant read performance degradation. One of the most critical reasons is access interference between read and write operations. This is because the read and write latency gaps are more pronounced for the latest NAND flash in SSDs. In this paper, an interference reduction scheme is proposed to improve performance. This is motivated by the observation from several server workloads, where read and write operations can be easily separated based on access characteristics. Considering that SSDs are always organized with many parallel units, the basic idea of this work is to partition the parallel units of the SSD into different areas and place data in the corresponding area according to access characteristics. Then, the interference can be optimized by issuing read and write requests to the different areas. To realize the above design, several approaches are proposed: First, an access characteristic-based data placement and migration method is proposed for read and write request separation. Second, to further adapt the parallel requirement for different workloads, a workload-based partitioning scheme is proposed to determine the number of parallel units for read and write areas. Finally, based on partitioned SSD, a hot-data driven wear-leveling method is further proposed to balance the wearing of parallel units in read and write areas. Experimental results show that partitioned SSD can significantly improve the read performance and wear-leveling of partitioned SSD can guarantee performance and lifetime.

Fusing In-storage and Near-storage Acceleration of Convolutional Neural Networks

Video analytics has a wide range of applications and has attracted much interest over the years. While it can be both computationally and energy-intensive, video analytics can greatly benefit from in/near memory compute. The practice of moving compute closer to memory has continued to show improvements to performance and energy consumption and is seeing increasing adoption. Recent advancements in solid state drives (SSDs) have incorporated near memory Field Programmable Gate Arrays (FPGAs) with shared access to the drive’s storage cells. These near memory FPGAs are capable of running operations required by video analytic pipelines such as object detection and template matching. These operations are typically executed using Convolutional Neural Networks (CNNs). A CNN is composed of multiple individually processed layers that perform various image processing tasks. Due to lack of resources, a layer may be partitioned into more manageable sub-layers. These sub-layers are then processed sequentially, however, some sub-layers can be processed simultaneously. Moreover, the storage cells within FPGA equipped SSDs are capable of being augmented with in-storage compute to accelerate CNN workloads and exploit the intra-parallelism within a CNN layer. To this end, we present our work, which leverages heterogeneous architectures to create an in/near-storage acceleration solution for video analytics. We designed a NAND flash accelerator and an FPGA accelerator, then mapped and evaluated several CNN benchmarks. We show how to utilize FPGAs, local DRAMs, and in-memory SSD compute to accelerate CNN workloads. Our work also demonstrates how to remove unnecessary memory transfers to save latency and energy.

Proactive Stripe Reconstruction to Improve Cache Use Efficiency of SSD-Based RAID Systems

Solid-State Drives (SSDs) exhibit different failure characteristics compared to conventional hard disk drives. In particular, the Bit Error Rate (BER) of an SSD increases as it bears more writes. Then, Parity-based Redundant Array of Inexpensive Disks (RAID) arrays composed from SSDs are introduced to address correlated failures. In the RAID-5 implementation, specifically, the process of parity generation (or update) associating with a data stripe, consists of read and write operations to the SSDs. Whenever a new update request comes to the RAID system, the related parity must be also updated and flushed onto the RAID component of SSD. Such frequent parity updates result in poor RAID performance and shorten the life-time of the SSDs. Consequently, a DRAM cache is commonly equipped accompanying with the RAID controller, called the parity cache, and used to buffer the parity chunks that are most frequently updated data, for boosting I/O performance. To better improve the use efficiency of the parity cache, this paper proposes a stripe reconstruction approach to minimize the number of parity updates on SSDs, thus boosting I/O performance of the SSD RAID system. When the currently updated stripe has both cold and hot updated data chunks, it will proactively carry out stripe reconstruction if we can find another matched stripe that also includes cold and hot update data chunks on the complementary RAID components. In the reconstruction process, we first group the cold data chunks of two matched stripes, to build a new stripe and flush the parity chunk on the RAID component. After that, the hot data chunks are organized as a new stripe as well, and its parity chunk is buffered in the parity cache. This results in better cache use efficiency, as it can reduce the number of parity updates on RAID components of SSDs, as well as proactively free up cache space for quickly absorbing subsequent write requests. In addition, the proposed method adjusts the target SSD of write requests based on stripe reconstructions through considering the I/O workload balance of all SSDs. Experimental results show that our proposal can reduce the number of parity chunk updates in SSDs by 2.3% and overall I/O latency by 12.2% on average, compared to state-of-the-art parity cache management techniques.

Preservation Strategies for Biodiversity Data

Preservation Strategies for Biodiversity Data