Lifetime Of Solid State Drives Research Articles

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 ×.

A Space-Efficient Fair Cache Scheme Based on Machine Learning for NVMe SSDs

Non-volatile memory express (NVMe) solid-state drives (SSDs) have been widely adopted in multi-tenant cloud computing environments or multi-programming systems. The on-board DRAM cache inside NVMe SSDs can efficiently reduce the disk accesses and extend the lifetime of SSDs. Current SSD cache management research either improves cache hit ratio while ignoring fairness, or improves fairness while sacrificing overall performance. In this paper, we present MLCache, a space-efficient shared cache management scheme for NVMe SSDs. By learning the impact of reuse distance on cache allocation, a workload-generic neural network model is built. At runtime, MLCache continuously monitors the reuse distance distribution for the neural network module to obtain space-efficient allocation decisions. MLCache also proposes an efficient parallel writing back strategy based on hit ratio and response time, to improve fairness. Experimental results show MLCache improves the write hit ratio when compared to baseline, and MLCache strongly safeguards the fairness of SSDs with parallel write-back and maintains a low level of degradation.

Adaptive Switch on Wear Leveling for Enhancing I/O Latency and Lifetime of High-Density SSDs

NAND flash-based high-density solid-state drives (SSDs) commonly have limited write endurance, as a single block becomes unavailable after a finite number of program/erase cycles. The (static) wear leveling algorithms migrate cold data to more worn blocks for yielding an even distribution of wears in SSDs. However, migrating cold data must delay normal I/O processing, and unnecessary wear leveling operations may damage SSD endurance as each operation causes a program/erase cycle. This article proposes an adaptive switch on wear leveling (called ASWL), to boost I/O performance and lifetime for high-density SSDs. The basic idea of ASWL is to adaptively switch the threshold function for intelligently triggering wear leveling operations at different stages of SSD lifetime. To this end, we build a mathematical model determining the wear leveling threshold condition with a dynamic manner, by considering the real-time factors of I/O intensity and the wear difference of SSD blocks. Through a series of emulation experiments on several realistic disk traces, we show that the proposed ASWL mechanism can not only greatly improve I/O performance but also noticeably extend the lifetime of high-density SSDs, in contrast to existing wear leveling methods.

Understanding and Exploiting the Full Potential of SSD Address Remapping

Duplicate writes are prevalent in storage systems, originating from data duplication, journaling, and data relocations, etc. As flash-based solid state drives (SSDs) have been widely deployed, duplicate writes can significantly degrade their performance and lifetime. Prior studies have proposed innovative approaches that exploit the address remapping utility inside an SSD to eliminate duplicate writes. However, remap operations modify the logical-to-physical (L2P) address mapping table while the physical-to-logical (P2L) mappings persisted on flash memory remain unchanged. Such inconsistency between L2P and P2L mappings may cause data corruption and has long been a major obstacle to utilize SSD address remapping. In this article, we propose a novel SSD design, called Remap-SSD-LH, that realizes the full potential of SSD address remapping. It provides a remap primitive, which allows the host software and SSD firmware to perform logical writes of duplicate data at almost zero cost. To ensure mapping consistency as well as fast mapping lookups, Remap-SSD-LH employs a local log scheme based on hybrid storage. A local log is maintained for each flash garbage collection unit to record relevant P2L mapping changes induced by remap operations. The logs are stored in small nonvolatile RAM (NVRAM), e.g., capacitor-protected DRAM, and can be destaged to flash memory if NVRAM is full. We verify Remap-SSD-LH on a software SSD emulator with three case studies: 1) intra-SSD deduplication; 2) SQLite journaling; and 3) F2FS cleaning. The experimental results show that Remap-SSD-LH can maximally and efficiently exploit address remapping to improve SSD performance and lifetime.

LLSM: A Lifetime-Aware Wear-Leveling for LSM-Tree on NAND Flash Memory

The advancement of nonvolatile memory (NVM) technology reduces the cost-per-unit of solid-state drives (SSDs). Flash memory-based SSDs have become ubiquitous because they provide better performance and energy efficiency than hard disk drives. However, it suffers from wear-out problems caused by the out-of-place updates that limit its lifetime. Log-structured merge tree (LSM-tree) is a level-based data structure that is widely used in many database systems because it eliminates the random write operations to the storage devices. By transferring the random write operations into sequential write operations, the write performance of hard disk drives can be improved. However, LSM-tree is not efficient for SSDs because it is not aware of the access characteristics of flash memory. Moreover, the level-based indexing strategy of the LSM-tree significantly shortens the lifetime of SSDs because the data must be frequently updated due to the compaction operations between different levels. In contrast to many previous works that focus on alleviating the write amplification on SSDs for the database systems implemented by LSM-tree, we propose LLSM, a lifetime-aware wear-leveling for LSM-tree on NAND flash memory with open-channel SSD. By considering the data access frequency of the LSM-tree between different levels, LLSM rethinks the block allocation strategy during the compaction to evenly erase all the blocks of SSD storage devices, prolonging the SSD lifetime. Moreover, a proactive swapping strategy is designed to reorganize the data blocks for resolving the potential wear-leveling issues caused by the behaviors of the LSM-tree. The extensive experiments show that the results of lifetime improvement are encouraging.

EddySuperblock: Improving NAND Flash Efficiency and Lifetime by Endurance-Driven Dynamic Superblock Management

Solid-state drives (SSDs) are widely used in current storage systems. SSD manufacturers usually aggregate physical blocks across planes into one superblock to improve SSD performance. However, due to the wear endurance variations among blocks and pages, the standard superblock scheme leads to lifetime and space waste of SSDs. Superblock granularity also causes higher and more uncertain garbage collection overhead. To address these problems, this work proposes endurance-driven dynamic superblock management called EddySuperblock to provide a longer lifetime and higher space utilization with a faster and deterministic response time, which designs a dynamic superblock organization strategy that links free strong blocks into one superblock according to the real-time wear index of each block and introduces page-level wear management to further unlock the lifetime potential of pages. To reduce the high garbage collection overhead caused by superblocks, a nonblocking parallel garbage collection policy is proposed, which can reclaim space and serve requests simultaneously with low overhead. We conduct a set of experiments on a real hardware experiment platform. Both the proposed EddySuperblock and representative superblock schemes have been implemented on the experiment platform. The experimental results show that our scheme greatly reduces the average system response time by 47.6% and prolongs the lifetime by 75.6% compared with the state-of-the-art superblock scheme, and the final-state flash utilization reaches 95.4%. In the end, this article presents the application status of our work in real-world scenarios.

Meta-Block: Exploiting Cross-Layer and Direct Storage Access for Decentralized Blockchain Storage Systems

Decentralized storage systems such as blockchain storage applications adopt the distributed storage technology and use distributed storage nodes to store the persistent data. For each off-chain storage node, key-value (KV) stores are normally used to manage data. As the most common data structure for KV store, Log Structured Merge Tree (LSM-Tree) eliminates random write operations and keeps acceptable read performance. Although LSM-Tree-based decentralized storage system can provide a secure and reliable storage platform, the unique feature of blockchain applications is not fully exploited. In blockchain storage applications, the generation of keys for KV stores is based on the encrypted data, and the key determines the allocation of data. Since the granularity for a read/write request at the level of blockchain storage platform is much smaller than that at the level of LSM-Tree or flash memory, a physical block in a solid-state drive (SSD) could be filled with data from different system users. This mixture of workloads will lead to the inefficient usage of physical spaces in the SSD and cause extra compaction operations for LSM-Tree. This paper presents <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Meta-Block</i> , a cross-layer and efficient storage management strategy for decentralized blockchain storage applications. Meta-Block utilizes rich functionalities provided by the system infrastructure of open-channel SSD to provide direct storage accesses for the off-chain storage node. The objective is to capture the features of blockchain storage applications and reduce unnecessary read and write operations across different storage layers. As a cross-layer design, Meta-Block redesigns the organization of LSM-Tree, which can effectively reduce the write amplification. We also design a data prefetching strategy to speed up the indexing and enable direct storage access. We demonstrate the viability of the proposed technique using a set of extensive experiments. Experimental results show that Meta-Block can effectively reduce the write amplification and extend the lifetime of SSDs in comparison with representative schemes.

SPOPB: Reducing solid state drive write traffic for flash‐based key‐value caching

AbstractFlash‐based key‐value (KV) caching has received increasing attention in recent years with the advantages of flash‐based solid state drives (SSDs) in capacity and cost. By caching most data in SSD, the caching system can eliminate lots of time‐consuming requests to back‐end data stores to provide low‐latency services. To adapt to the unique technical constraints of flash memory, flash‐based KV caching adopts a slab‐based log‐structured management scheme in which the slab is the basic storage unit, and uses a small memory space as a write buffer to eliminate small random writes to SSD for consistent performance and increased lifetime of SSD. However, we have observed that under update‐intensive workloads with strong temporal locality, the slab‐based management in flash‐based KV caching introduces substantial SSD write traffic because of indistinguishable SSD flushing of hot items in slabs, which shortens the SSD lifetime and degrades the performance with increased erase operations. In this article, we first analyze the SSD write traffic in the flash‐based KV caching, and then propose a novel slab popularity‐based storage management scheme‐SPOPB, to extend SSD lifetime and improve performance. Our scheme identifies hot items using a self‐adaptive threshold to reorganize and classify slabs with both the hotness and size of items. Then SPOPB filters and retains the popular slabs containing hot items in the write buffer with redesigned replacement policy to reduce the SSD write traffic. Our experiments show that our design can effectively reduce the SSD write traffic by 63.6%, the erase counts by 55.6%, and improve the performance by 42%.

An Efficient Data Migration Scheme to Optimize Garbage Collection in SSDs

Garbage collection (GC) is time consuming and frequently executed all over the lifetime of solid-state drives (SSDs), which has a significant impact on system performance. Manufactures provide the copyback that directly transfers data within the same plane to accelerate data migration in GC. However, the introduction of copyback leads to two issues: 1) high detection overhead of copyback feasibility (whether data are carried out via copyback with guaranteed reliability) and 2) interplane unbalanced wear distribution. In this article, we first explore copyback error characteristics on the real NAND flash chip, then propose a fast GC scheme called FastGC . It utilizes copyback error characteristics to efficiently detect the copyback feasibility of data instead of transferring out all valid data for detecting. FastGC further utilizes a data migration leveler which aims at relieving migration overhead per GC to realize the wear leveling. Regarding data migrated via external data move (EDM), FastGC takes data coldness and erase counts of planes into consideration to even out the number of migrating data per plane and prolong the lifetime of SSDs. SSDsim, a validate simulation is used to implement FastGC and comprehensive experiments are carried out with various enterprise workloads to evaluate the system performance and the wear difference of SSDs. The experimental results in the SSDsim show the FastGC greatly promotes system performance and the wear leveling up to 46.68% and 12X, respectively, compared to the traditional copyback-based GC.

SmartHeating: On the Performance and Lifetime Improvement of Self-Healing SSDs

In NAND flash memory-based solid-state drives (SSDs), during the idle time between the consecutive program/erase cycles (dwell time), the dielectric damage of flash cell can be partially repaired, also known as the self-recovery effect. As the effectiveness of the self-recovery effect can be improved under high temperature, self-healing SSDs are proven feasible to extend the flash endurance significantly. However, current self-healing SSDs perform the heating operations on all the worn-out blocks without considering the data retention requirement, and measures the lifetime of flash memory based on the worst-case self-recovery effect, leading to some unnecessary heating operations and the degraded performance. We propose SmartHeating, a smart heating scheme that exploits the dwell time variation and the write hotness variation to improve the I/O performance and the lifetime of self-healing SSDs. SmartHeating tracks the dwell time of all worn-out flash blocks, predicts their self-recovery effect and reliability, and avoids performing heating operations on the worn-out flash blocks that still have strong flash reliability. In addition, by exploiting the data hotness variation, SmartHeating only heats the worn-out flash blocks that store write-cold data, while allocating write-hot data to a small portion of worn-out flash blocks with negligible refresh overhead. The experimental results show that SmartHeating reduces the number of heating operations by 12.5% on average, boosts I/O performance of flash storage systems by 21.0%, and improves the lifetime of flash memory by $1.20\times $ compared with conventional heating scheme.

Leveraging partial-refresh for performance and lifetime improvement of 3D NAND flash memory in cyber-physical systems

Abstract Three-dimensional (3D) NAND flash memory-based solid state drives (SSDs) have been widely adopted in cyber-physical systems, due to its performance benefits and scalability. Although 3D flash rapidly increases storage capacity by stacking flash cells in the vertical direction, it faces severe retention errors as well. Periodic refreshing, while effectively mitigating the retention issues, seriously degrades the storage performance and the endurance of 3D NAND flash memory. To address the above challenge, we propose partial-refresh (PR), a novel lightweight data refresh scheme for 3D NAND flash memory in cyber-physical systems. PR leverages LDPC detectability to identify cells that are more vulnerable to errors. By moving these susceptible bits to new pages, PR avoids copying an entire page, and reduces the refresh cost and prolongs the SSD lifetime. Our experimental results show that, on average, a PR-aware flash memory improves refresh performance by 28.2% and extends the SSD lifetime by 4.6% over the state-of-the-art while preserving the high data reliability.

Measurement and Analysis of SSD Reliability Data Based on Accelerated Endurance Test

In recent years, NAND Flash-based solid-state drives (SSDs) have become more widely used in data centers and consumer markets. Data centers generally choose to provide high-quality storage services by deploying a large number of SSDs, but there are no effective preventive measures to reduce the impact of SSD failures currently. Some existing studies have analyzed the relevant factors related to SSD failures from different angles, but the characteristics of reliability changes exhibited by SSD throughout the life cycle have not been explored in depth. On the other hand, although the 3D manufacturing process has increased the storage density of the SSD, the mutual influence between the flash units has also increased, resulting in severe degradation of the performance and lifetime of the SSD. Therefore, in order to fully understand the reliability varying process of SSD throughout the life cycle, we first designed an SSD lifetime endurance test method, then conducted the endurance test and collected the reliability data for the entire life cycle of the 3D TLC SSD in the laboratory environment with reference to the JEDEC standard. Through the analysis of experimental data and its statistical correlation, it is found that SSD will produce a large number of uncorrectable errors before reaching the endurance limit, and there will be a phenomenon of continuous high operating temperature, as well as showing some intrinsic relationships about SSD reliability data. The findings in this paper are valuable for identifying whether an SSD is going to fail.

Preserving SSD lifetime in deep learning applications with delta snapshots

In large-scale deep learning applications, SSDs (Solid State Drives) have been widely adopted to speed up the training. However, a snapshot process is periodically performed in a deep learning application, by which a great number of training parameters (at the TB level) need to be written to SSDs; thus, it poses serious challenges for the lifetime of SSDs. In this paper, we for the first time design a mechanism, called delta snapshot, that can effectively reduce the overall amount of data written to an SSD during the training phase so as to preserve its lifetime. Delta snapshot exploits the redundant information between snapshots. Specifically, we observe that the exponent part and the most significant bits of the mantissa change very little between two consecutive snapshots. Based on this, we develop effective mechanism to compress the redundant bits of snapshots to reduce the size of the written data. Experimental results showed that our technique can reduce the overall amount of written data by 31% and the erase operations by 27%, with a negligible time overhead in the training phase.

Revive Bad Flash-Memory Pages by HLC Scheme

In recent years, flash memory has been widely used in embedded systems, portable devices, and high-performance storage products due to its nonvolatility, shock resistance, low power consumption, and high performance natures. To reduce the product cost, multi-level-cell (MLC) flash memory has been proposed; compared with the traditional single-level-cell (SLC) flash memory that only stores one bit of data per cell, each MLC cell can store two or more bits of data. Thus MLC can achieve a larger capacity and reduce the cost per unit. However, MLC also suffers from the degradation in both performance and reliability. In this paper, we try to enhance the reliability and reduce the product cost of flash-memory-based solid-state drive (SSD) from a totally different perspective. We propose the half-level-cell (HLC) scheme to manage and reuse the worn-out space in SSD; through our management scheme, the system can treat two bad pages as a normal page without sacrificing performance and reliability. The proposed scheme is purely on software/firmware-level, thus there is no need to change the hardware. The experiment results show that the lifetime of SSD with our proposed HLC scheme can be extended to 50.56% under the Windows workload and up to 65.45% under the multimedia workload. When we apply the HLC scheme to flash-memory cache of hybrid storage systems, the response time can be improved up to 20.57%.

WARD: Wear Aware RAID Design Within SSDs

Redundant arrays of independent disk (RAID) is an efficient approach to relieve reliability sacrifice caused by aggressive scale-out of solid state drives (SSDs). Unfortunately, RAID is unfriendly to SSDs due to redundant parity write and data rebuilding. This paper proposes a wear aware RAID design for SSDs, called WARD , which: 1) adaptively organizes RAID stripes according to real-time interblock unbalanced wear for relieving high performance and storage overhead caused by parity data and 2) migrates blocks about to break in advance and leaves these blocks unused to reduce data rebuilding overhead. An efficient block wear detection scheme is employed to detect block wear during the whole lifetime of SSDs. Beginning with a large stripe width RAID instead of the redundant worst-case RAID, WARD reorganizes RAID stripes once wear blocks with high bit error rates come out. WARD divides the original stripe into several short width RAID stripes according to the number of wear blocks and separates all wear blocks into different stripes. This not only reduces parity redundancy but also provides high reliability to avoid more than RAID recoverable error-prone chunks remaining in one stripe. For high wear blocks tending to wear-out, data in them are migrated in advance and then the blocks are left unused, which efficiently avoids performance shock caused by data rebuilding. A reliability model considering interblock unbalanced wear is proposed and reveals that WARD provides a high and stable reliability and greatly prolongs the lifetime of SSDs. Comprehensive experiments based on an SSDsim derivative simulator are carried out and experiment results show that WARD considerably improves system performance compared to the worst-case RAID.

CRIM: Conditional Remapping to Improve the Reliability of Solid-State Drives with Minimizing Lifetime Loss

Solid-state drive (SSD) becomes popular as the main storage device. However, over time, the reliability of SSD degrades due to bit errors, which poses a serious issue. The periodic remapping (PR) has been suggested to overcome the issue, but it still has a critical weakness as PR increases lifetime loss. Therefore, we propose the conditional remapping invocation method (CRIM) to sustain reliability without lifetime loss. CRIM uses a probability-based threshold to determine the condition of invoking remapping operation. We evaluate the effectiveness of CRIM using the real workload trace data. In our experiments, we show that CRIM can extend a lifetime of SSD more than PR by up to 12.6% to 17.9% of 5-year warranty time. In addition, we show that CRIM can reduce the bit error probability of SSD by up to 73 times in terms of typical bit error rate in comparison with PR.

Solid-State Drives: Memory Driven Design Methodologies for Optimal Performance

Solid-state drives (SSDs) faced an astonishing development in the last few years, becoming the cornerstone to new paradigms and markets of the information technology, such as cloud computing and big data centers. So far, the SSD design approach has focused on the optimization of the Flash translation layer, the firmware devoted to fulfill the compatibility with traditional hard-disk drives. For hyperscaled SSDs this strategy is no longer valid since their performance and reliability are strictly linked to that of the NAND Flash memories that constitute the storage medium, in particular when the multilevel cell paradigm is considered. For this reason, the design flow must follow a bottom-up approach that, starting from an accurate knowledge of the time and use dependent reliability of the NAND Flash memories, selects the most appropriate error correction strategy to extend the SSD lifetime while reducing its performance degradation. Then, the design flow moves to that of the SSD controller and of the interface toward the host where the application is running. This paper will thoroughly discuss this bottom-up approach, and finally, it will show how it is possible to leverage new approaches, such as the software-defined storage system that, by exploiting a hardware/software codesign of the SSD controller architecture and of the host application, will be able to revolutionize the traditional computer/memory interaction.

Stochastic modeling for performance and availability evaluation of hybrid storage systems

Abstract Improvements in computational systems may be constrained by the efficiency of storage drives. Therefore, replacing hard disk drives (HDD) with solid-state drives (SSD) may also be an effective way to improve system performance, but the higher cost per gigabyte and reduced lifetime of SSDs hinder a thorough replacement. To mitigate these issues, several architectures have been conceived based on hybrid storage systems, but performance and availability models have not been proposed to better assess such different architectures. This paper presents an approach based on stochastic models (i.e., stochastic Petri nets and reliability block diagrams) for performance and availability evaluation of hybrid storage systems. The proposed models can represent write and read operations, and they may estimate response time, throughput and availability. A case study based on OpenStack Swift platform is adopted to demonstrate the feasibility of the proposed approach.

ErasuCrypto: A Light-weight Secure Data Deletion Scheme for Solid State Drives

Abstract Securely deleting invalid data from secondary storage is critical to protect users’ data privacy against unauthorized accesses. However, secure deletion is very costly for solid state drives (SSDs), which unlike hard disks do not support in-place update. When applied to SSDs, both erasure-based and cryptography-based secure deletion methods inevitably incur large amount of valid data migrations and/or block erasures, which not only introduce extra latency and energy consumption, but also harm SSD lifetime. This paper proposes ErasuCrypto, a light-weight secure deletion framework with low block erasure and data migration overhead. ErasuCrypto integrates both erasurebased and encryption-based data deletion methods and flexibly selects the more cost-effective one to securely delete invalid data. We formulate a deletion cost minimization problem and give a greedy heuristic as the starting point. We further show that the problem can be reduced to a maximum-edge biclique finding problem, which can be effectively solved with existing heuristics. Experiments on real-world benchmarks show that ErasuCrypto can reduce the secure deletion cost of erasurebased scheme by 71% and the cost of cryptographybased scheme by 37%, while guaranteeing 100% security by deleting all the invalid data.

Effective Lifetime-Aware Dynamic Throttling for NAND Flash-Based SSDs

NAND flash-based solid-state drives (SSDs) are increasingly popular in enterprise server systems because of their advantages over hard disk drives such as higher performance and lower power consumption. However, the decreasing write endurance and the unpredictable lifetime remains to be a serious obstacle to their wider adoption in enterprise systems. In this paper, we propose effective lifetime-aware dynamic throttling, called LADY, which guarantees the required storage lifetime by intentionally throttling the write performance of SSDs with consideration of the effective write endurance of NAND flash memory. Unlike existing static throttling, LADY makes throttling decisions based on the characteristics of a workload so that the required SSD lifetime can be guaranteed with less performance degradation. LADY also exploits the improvement on write endurance depending on the NAND program speed and the recovery effects of floating-gate transistors, thereby maximally utilizing the available write endurance of NAND flash while mitigating the decreasing write endurance problem. Our experimental results show that LADY improves write performance by 4.7x with small write response time variations over existing static throttling while guaranteeing the required SSD lifetime.