Solid State Drives Research Articles

WEC: Improving Durability of SSD Cache Drives by Caching Write-Efficient Data

Serving as cache disks, flash-based solid-state drives (SSDs) can significantly boost the performance of read-intensive applications. However, frequent data updating, the necessary condition for classical replacement algorithms (e.g., LRU, MQ, LIRS, and ARC) to achieve a high hit rate, makes SSDs wear out quickly. To address this problem, we propose a new approach—write-efficient caching (WEC)—to greatly improve the write durability of SSD cache. WEC is conducive to reducing the total number of writes issued to SSDs while achieving high hit rates. WEC takes two steps to improve write durability and performance of SSD cache. First, WEC discovers write-efficient data, which tend to be active for a long time period and to be frequently accessed. Second, WEC keeps the write-efficient data in SSDs long enough to avoid excessive number of unnecessary updates. Our findings based on a wide range of popular real-world traces show that write-efficient data does exist in a wide range of popular read-intensive applications. Our experimental results indicate that compared with the classical algorithms, WEC judiciously improves the mean hits of each written block by approximately two orders of magnitude while exhibiting similar or even higher hit rates.

Read/write-optimized tree indexing for solid-state drives

Flash-memory-based solid-state drives (SSDs) are used widely for secondary storage. To be effective for SSDs, traditional indices have to be redesigned to cope with the special properties of flash memory, such as asymmetric read/write latencies (fast reads and slow writes) and out-of-place updates. Previous flash-optimized indices focus mainly on reducing random writes to SSDs, which is typically accomplished at the expense of a substantial number of extra reads. However, modern SSDs show a narrowing gap between read and write speeds, and read operations on SSDs increasingly affect the overall performance of indices on SSDs. As a consequence, how to optimize SSD-aware indices by reducing both write and read costs is a pertinent and open challenge. We propose a new tree index for SSDs that is able to reduce both writes and extra reads. In particular, we use an update buffer and overflow pages to reduce random writes, and we further exploit Bloom filters to reduce the extra reads to the overflow nodes in the tree. With this mechanism, we construct a read/write-optimized index that is capable of offering better overall performance than previous flash-aware indices. In addition, we present an analysis of the proposed index and show that the read and write costs of the operations on the index can be balanced by only tuning the false-positive rate of the Bloom filters. Our experimental results suggest that our proposal is efficient and represents an improvement over existing methods.

O(1) 크래시 복구 수행시간을 갖는 FTL의 설계와 구현

The capacity of flash-based storage such as Solid State Drive(SSD) and embedded Multi Media Card(eMMC) is ever-increasing because of the needs from the end-users. However, if a flash-based storage crashes, such as during power failure, the flash translation layer(FTL) is responsible for the crash recovery based on the entire flash memory. The recovery time increases as the capacity of the flash-based storages increases. We propose O1FTL with O(1) crash recovery time that is independent of the flash capacity. O1FTL adopts the working area technique suggested for the flash file system and evaluates the design on a real hardware platform. The results show that O1FTL achieves a crash recovery time that is independent of the capacity and the overhead, in terms of I/O performance, and achieves a low P/E cycle.

High-Performance and Lightweight Transaction Support in Flash-Based SSDs

Flash memory has accelerated the architectural evolution of storage systems with its unique characteristics compared to magnetic disks. The no-overwrite property of flash memory naturally supports transactions, a commonly used mechanism in systems to provide consistency. However, existing embedded transaction designs in flash-based Solid State Drives (SSDs) either limit the transaction concurrency or introduce high overhead in tracking transaction states. This leads to low or unstable SSD performance. In this paper, we propose a transactional SSD (TxSSD) architecture, LightTx, to enable better concurrency and low overhead. First, LightTx improves transaction concurrency arbitrarily by using a page-independent commit protocol. Second, LightTx tracks the recent updates by leveraging the near-log-structured update property of SSDs and periodically retires dead transactions to reduce the transaction state tracking cost. Experiments show that LightTx achieves nearly the lowest overhead in garbage collection, memory consumption and mapping persistence compared to existing embedded transaction designs. LightTx also provides up to 20.6 percent performance improvement due to improved transaction concurrency.

SSDExplorer: A Virtual Platform for Performance/Reliability-Oriented Fine-Grained Design Space Exploration of Solid State Drives

Currently available electronic design automation tools for design space exploration of solid state drives (SSDs) are not able to assess: 1) the device architecture inefficiencies; 2) architecture overdesign for a target performance; and 3) performance degradation caused by the disk usage. These tools feature either an overly high abstraction modeling strategy or lack the required flexibility to perform design exploration. To overcome these problems, this paper proposes SSDExplorer, a tool for fine-grained yet reasonably fast design space exploration of different SSD architectures highlighting possible bottlenecks. To prove its accuracy SSDExplorer has been validated with two real SSDs. SSDExplorer efficiency has been assessed by evaluating the impact of the NAND flash read retry algorithm impact on the SSD performance as a function of its internal architecture.

VSSD

Performance isolation is critical in shared storage systems, a popular storage solution. In a shared storage system, interference between requests from different users can affect the accuracy of I/O cost accounting, resulting in poor performance isolation. Recently, NAND flash-memory-based solid-state drives (SSDs) have been increasingly used in shared storage systems. However, interference in SSD-based shared storage systems has not been addressed. In this article, two types of interference, namely, queuing delay (QD) interference and garbage collection (GC) interference, are identified in a shared SSD. Additionally, a framework called VSSD is proposed to address these types of interference. VSSD is composed of two components: the FACO credit-based I/O scheduler designed to address QD interference and the ViSA flash translation layer designed to address GC interference. The VSSD framework aims to be implemented in the firmware running on an SSD controller. With VSSD, interference in an SSD can be eliminated and performance isolation can be ensured. Both synthetic and application workloads are used to evaluate the effectiveness of the proposed VSSD framework. The performance results show the following. First, QD and GC interference exists and can result in poor performance isolation between users on SSD-based shared storage systems. Second, VSSD is effective in eliminating the interference and achieving performance isolation between users. Third, the overhead of VSSD is insignificant.

SSD 수명 관점에서 리눅스 I/O 스택에 대한 실험적 분석

낸드 플래시 기반의 SSD (Solid-State Drive)는 HDD (Hard Disk Drive) 대비 월등한 성능에도 불구하고 쓰기 회수 제한이라는 태생적 단점을 가지고 있다. 이로 인해 SSD의 수명은 워크로드에 의해 결정되어 SSD의 기술 변화 추세인 SLC (Single Level Cell) 에서 MLC (Multi Level Cell) 로의 전환, MLC에서 TLC (Triple Level Cell) 로의 전환에 있어 큰 도전이 될 수 있다. 기존 연구들은 주로 wear-leveling 또는 하드웨어 아키텍처 측면에서 SSD의 수명 개선을 다루었으나, 본 논문에서는 호스트가 요청한 쓰기에 대해 SSD가 낸드플래시 메모리를 통해 처리하는 수명관점의 효율성을 대변하는 WAF (Write Amplification Factor) 관점에서 Host I/O 스택 중 파일 시스템, I/O 스케줄러, 링크 전력에 대해 JEDEC 엔터프라이즈 워크로드를 이용해 I/O 스택 최적 구성에 대해 실험적 분석을 수행하였다. WAF는 SSD의 FTL의 효율성을 측정하는 지표로 수명관점에서 가장 객관적으로 사용한다. I/O 스택에 대한 수명 관점의 최적 구성은 MinPower-Dead-XFS로 최대 성능 조합인 MaxPower-Cfq-Ext4에 비해 성능은 13% 감소하였지만 수명은 2.6 배 연장됨을 확인하였다. 이는 I/O 스택의 최적화 구성에 있어, SSD 성능 관점뿐만 아니라 수명 관점의 고려에 대한 유의미를 입증한다.

Will solid-state drives accelerate your bioinformatics? In-depth profiling, performance analysis and beyond.

A wide variety of large-scale data have been produced in bioinformatics. In response, the need for efficient handling of biomedical big data has been partly met by parallel computing. However, the time demand of many bioinformatics programs still remains high for large-scale practical uses because of factors that hinder acceleration by parallelization. Recently, new generations of storage devices have emerged, such as NAND flash-based solid-state drives (SSDs), and with the renewed interest in near-data processing, they are increasingly becoming acceleration methods that can accompany parallel processing. In certain cases, a simple drop-in replacement of hard disk drives by SSDs results in dramatic speedup. Despite the various advantages and continuous cost reduction of SSDs, there has been little review of SSD-based profiling and performance exploration of important but time-consuming bioinformatics programs. For an informative review, we perform in-depth profiling and analysis of 23 key bioinformatics programs using multiple types of devices. Based on the insight we obtain from this research, we further discuss issues related to design and optimize bioinformatics algorithms and pipelines to fully exploit SSDs. The programs we profile cover traditional and emerging areas of importance, such as alignment, assembly, mapping, expression analysis, variant calling and metagenomics. We explain how acceleration by parallelization can be combined with SSDs for improved performance and also how using SSDs can expedite important bioinformatics pipelines, such as variant calling by the Genome Analysis Toolkit and transcriptome analysis using RNA sequencing. We hope that this review can provide useful directions and tips to accompany future bioinformatics algorithm design procedures that properly consider new generations of powerful storage devices.

Quality-of-Service Implications of Enhanced Program Algorithms for Charge-Trapping NAND in Future Solid-State Drives

Three-dimensional nand memory devices based on charge trapping (CT) technology represent the most promising solution for hyperscaled solid-state drives (SSDs). However, the intrinsic low reliability offered by that storage medium leads to a high number of errors requiring an extensive use of complex error correction codes (ECCs) and advanced read algorithms such as read retry. This materializes in an overall reduction in the SSD's QoS. In order to limit the error number, enhanced program algorithms that are able to improve the reliability figures of CT memory devices have been introduced. In this paper, the impact of such program algorithms combined with read retry and the ECC is experimentally characterized on CT- nand arrays. The results are then exploited for cosimulations at the system level, assessing the reliability, performance, and QoS of future SSDs integrating CT-based memory devices.

H-Hash: A Hash Index Structure for Flash-Based Solid State Drives

Flash-based solid state drives (SSDs) have been widely used as storage medium because of their fast access speed, high reliability and low power consumption. In spite of these advantages, they inherit distinct characteristics such as no in-place updates, asymmetric operation speed and unit because they consist of NAND flash memories. Therefore, a disk-based hash index structure may result in severe performance degradation if it is directly deployed on a NAND flash memory-based storage system. In this paper, we propose a new hybrid hash index structure for flash-based SSDs. It delays and reduces the split operations which cause slow block erasure and additional read and write operations by exploiting overflow buckets according to the ratio of updates and deletions. Through various performance evaluations, we show the superiority of our proposed index structure by comparing it to other hash index structures for flash-based SSDs.

On endurance and performance of erasure codes in SSD-based storage systems

Erasure codes are widely used in data storage systems to protect against disk failures. Employing erasure codes in an array of Solid-State Drives (SSDs) in storage systems necessitates designers to revisit different characteristics in comparison to Hard Disk Drives (HDDs), due to non-mechanical property of SSDs. One of the most important characteristics of SSDs is their limitation on the number of Program/Erase (P/E) cycles. By taking into account the characteristics of SSDs, this paper presents a comprehensive analysis to investigate the effects of three well-known erasure codes on the endurance and performance of SSD-based disk subsystems. The three erasure codes, i.e., Reed–Solomon, EVENODD, and RDP are implemented on the SSD-extension of DiskSim simulator. The results show that the endurance and performance of Reed–Solomon are on average 90% and 60% higher than other erasure codes, respectively. Additionally, the three erasure codes are compared in terms of different stripe unit sizes, number of disks, and various request sizes. The results show that configuring a disk array with a 4KB stripe unit size will improve the endurance and performance of EVENODD by 1.8× and 2.9×, respectively, as compared to 128KB stripe unit size.

Hetero-DB: Next Generation High-Performance Database Systems by Best Utilizing Heterogeneous Computing and Storage Resources

With recent advancement on hardware technologies, new general-purpose high-performance devices have been widely adopted, such as the graphics processing unit (GPU) and solid state drive (SSD). GPU may offer an order of higher throughput for applications with massive data parallelism, compared with the multicore CPU. Moreover, new storage device SSD is also capable of offering a much higher I/O throughput and lower latency than a traditional hard disk device (HDD). These new hardware devices can significantly boost the performance of many applications; thus the database community has been actively engaging in adopting them into database systems. However, the performance benefit cannot be easily reaped if the new hardwares are improperly used. In this paper, we propose Hetero-DB, a high-performance database system by exploiting both the characteristics of the database system and the special properties of the new hardware devices in system’s design and implementation. Hetero-DB develops a GPU-aware query execution engine with GPU device memory management and query scheduling mechanism to support concurrent query execution. Furthermore, with the SSD-HDD hybrid storage system, we redesign the storage engine by organizing HDD and SSD into a two-level caching hierarchy in Hetero-DB. To best utilize the hybrid hardware devices, the semantic information that is critical for storage I/O is identified and passed to the storage manager, which has a great potential to improve the efficiency and performance. Hetero-DB aims to maximize the performance benefits of GPU and SSD, and demonstrates the effectiveness for designing next generation database systems.

A Large-Scale Study of Flash Memory Failures in the Field

Servers use flash memory based solid state drives (SSDs) as a high-performance alternative to hard disk drives to store persistent data. Unfortunately, recent increases in flash density have also brought about decreases in chip-level reliability. In a data center environment, flash-based SSD failures can lead to downtime and, in the worst case, data loss. As a result, it is important to understand flash memory reliability characteristics over flash lifetime in a realistic production data center environment running modern applications and system software. This paper presents the first large-scale study of flash-based SSD reliability in the field. We analyze data collected across a majority of flash-based solid state drives at Facebook data centers over nearly four years and many millions of operational hours in order to understand failure properties and trends of flash-based SSDs. Our study considers a variety of SSD characteristics, including: the amount of data written to and read from flash chips; how data is mapped within the SSD address space; the amount of data copied, erased, and discarded by the flash controller; and flash board temperature and bus power. Based on our field analysis of how flash memory errors manifest when running modern workloads on modern SSDs, this paper is the first to make several major observations: (1) SSD failure rates do not increase monotonically with flash chip wear; instead they go through several distinct periods corresponding to how failures emerge and are subsequently detected, (2) the effects of read disturbance errors are not prevalent in the field, (3) sparse logical data layout across an SSD's physical address space (e.g., non-contiguous data), as measured by the amount of metadata required to track logical address translations stored in an SSD-internal DRAM buffer, can greatly affect SSD failure rate, (4) higher temperatures lead to higher failure rates, but techniques that throttle SSD operation appear to greatly reduce the negative reliability impact of higher temperatures, and (5) data written by the operating system to flash-based SSDs does not always accurately indicate the amount of wear induced on flash cells due to optimizations in the SSD controller and buffering employed in the system software. We hope that the findings of this first large-scale flash memory reliability study can inspire others to develop other publicly-available analyses and novel flash reliability solutions.

응용프로그램의 기동시간 단축을 위한 파일 시스템 수준의 SSD 캐싱 기법

Application launch time is an important performance metric to user experience in desktop and laptop environment, which mostly depends on the performance of secondary storage. Application launch times can be reduced by utilizing solid-state drive (SSD) instead of hard disk drive (HDD). However, considering a cost-performance trade-off, utilizing SSDs as caches for slow HDDs is a practicable alternative in reducing the application launch times. We propose a new SSD caching scheme which migrates data blocks from HDDs to SSDs. Our scheme operates entirely in the file system level and does not require an extra layer for mapping SSD-cached data that is essential in most other schemes. In particular, our scheme does not incur mapping overheads that cause significant burdens on the main memory, CPU, and SSD space for mapping table. Experimental results conducted with 8 popular applications demonstrate our scheme yields 56% of performance gain in application launch, when data blocks along with metadata are migrated.

Advanced error-prediction LDPC with temperature compensation for highly reliable SSDs

To improve the reliability of NAND Flash memory based solid-state drives (SSDs), error-prediction LDPC (EP-LDPC) has been proposed for multi-level-cell (MLC) NAND Flash memory (Tanakamaru et al., 2012, 2013), which is effective for long retention times. However, EP-LDPC is not as effective for triple-level cell (TLC) NAND Flash memory, because TLC NAND Flash has higher error rates and is more sensitive to program-disturb error. Therefore, advanced error-prediction LDPC (AEP-LDPC) has been proposed for TLC NAND Flash memory (Tokutomi et al., 2014). AEP-LDPC can correct errors more accurately by precisely describing the error phenomena. In this paper, the effects of AEP-LDPC are investigated in a 2×nm TLC NAND Flash memory with temperature characterization. Compared with LDPC-with-BER-only, the SSD’s data-retention time is increased by 3.4× and 9.5× at room-temperature (RT) and 85°C, respectively. Similarly, the acceptable BER is increased by 1.8× and 2.3×, respectively. Moreover, AEP-LDPC can correct errors with pre-determined tables made at higher temperatures to shorten the measurement time before shipping. Furthermore, it is found that one table can cover behavior over a range of temperatures in AEP-LDPC. As a result, the total table size can be reduced to 777kBytes, which makes this approach more practical.

Optimizing the Hadoop MapReduce Framework with high-performance storage devices

Solid-state drives (SSDs) are an attractive alternative to hard disk drives (HDDs) to accelerate the Hadoop MapReduce Framework. However, the SSD characteristics and today's Hadoop framework exhibit mismatches that impede indiscriminate SSD integration. This paper explores how to optimize a Hadoop MapReduce Framework with SSDs in terms of performance, cost, and energy consumption. It identifies extensible best practices that can exploit SSD benefits within Hadoop when combined with high network bandwidth and increased parallel storage access. Our Terasort benchmark results demonstrate that Hadoop currently does not sufficiently exploit SSD throughput. Hence, using faster SSDs in Hadoop does not enhance its performance. We show that SSDs presently deliver significant efficiency when storing intermediate Hadoop data, leaving HDDs for Hadoop Distributed File System (HDFS). The proposed configuration is optimized with the JVM reuse option and frequent heartbeat interval option. Moreover, we examined the performance of a state-of-the-art non-volatile memory express interface SSD within the Hadoop MapReduce Framework. While HDFS read and write throughput increases with high-performance SSDs, achieving complete system performance improvement requires carefully balancing CPU, network, and storage resource capabilities at a system level.

AMC: an adaptive multi‐level cache algorithm in hybrid storage systems

SummaryHybrid storage systems that consist of flash‐based solid state drives (SSDs) and traditional disks are now widely used. In hybrid storage systems, there exists a two‐level cache hierarchy that regard dynamic random access memory (DRAM) as the first level cache and SSD as the second level cache for disk storage. However, this two‐level cache hierarchy typically uses independent cache replacement policies for each level, which makes cache resource management inefficient and reduces system performance. In this paper, we propose a novel adaptive multi‐level cache (AMC) replacement algorithm in hybrid storage systems. The AMC algorithm adaptively adjusts cache blocks between DRAM and SSD cache levels using an integrated solution. AMC uses combined selective promote and demote operations to dynamically determine the level in which the blocks are to be cached. In this manner, the AMC algorithm achieves multi‐level cache exclusiveness and makes cache resource management more efficient. By using real‐life storage traces, our evaluation shows the proposed algorithm improves hybrid multi‐level cache performance and also increases the SSD lifetime compared with traditional multi‐level cache replacement algorithms. Copyright © 2015 John Wiley & Sons, Ltd.

SSD-Tailor: Automated Customization System for Solid-State Drives

Enterprise servers require customized solid-state drives (SSDs) to satisfy their specialized I/O performance and reliability requirements. For effective use of SSDs for enterprise purposes, SSDs must be designed considering requirements such as those related to performance, lifetime, and cost constraints. However, SSDs have numerous hardware and software design options, such as flash memory types and block allocation methods, which have not been well analyzed yet, but on which the SSD performance depends. Furthermore, there is no methodology for determining the optimal design for a particular I/O workload. This paper proposes SSD-Tailor, a customization tool for SSDs. SSD-Tailor determines a near-optimal set of design options for a given workload. SSD designers can use SSD-Tailor to customize SSDs in the early design stage to meet the customer requirements. We evaluate SSD-Tailor with nine I/O workload traces collected from real-world enterprise servers. We observe that SSD-Tailor finds near-optimal SSD designs for these workloads by exploring only about 1% of the entire set of design candidates. We also show that the near-optimal designs increase the average I/O operations per second by up to 17% and decrease the average response time by up to 163% as compared to an SSD with a general design.

Effective flash-based SSD caching for high performance home cloud server

In the home cloud environment, the storage performance of home cloud servers, which govern connected devices and provide resources with virtualization features, is critical to improve the end-user experience. To improve the storage performance of virtualized home cloud servers in a cost-effective manner, caching schemes using flash-based solid state drives (SSD) have been widely studied. Although previous studies successfully narrow the speed gap between memory and hard disk drives, they only focused on how to manage the cache space, but were less interested in how to use the cache space efficiently taking into account the characteristics of flash-based SSD. Moreover, SSD caching is used as a read-only cache due to two well-known limitations of SSD: slow write and limited lifespan. Since storage access in virtual machines is performed in a more complex and costly manner, the limitations of SSD affect more significantly the storage performance. This paper proposes a novel SSD caching scheme and virtual disk image format, named sequential virtual disk (SVD), for achieving high-performance home cloud environments. The proposed techniques are based on the workload characteristics, in which synchronous random writes dominate, while taking into consideration the characteristics of flash memory and storage stack of the virtualized systems. Unlike previous studies, SSD is used as a read-write cache in the proposed caching scheme to effectively mitigate the performance degradation of synchronous random writes. The prototype was evaluated with some realistic workloads, through which the developed scheme was shown to allow improvement of the storage access performance by 21% to 112%, with reduction in the number of erasures on SSD by about 56% on average.

SSD 강제진동 시 더미 솔더 볼 효과에 의한 피로수명 예측

This research proposes a method to estimate the fatigue life of SSD(solid-state drive) due to the effect of dummy solder ball under forced vibration. A finite element model of the SSD was developed to simulate the forced vibration and a modal testing was performed to verify the developed finite element model. Fatigue life of the SSD under vibration was experimentally determined according to JEDEC standard in which the SSD was excited by a sinusoidal sweep vibration within the narrow frequency band around the first natural frequency until the SSD fails. Basquin’s equation was introduced to estimate the fatigue life of the SSD due to the effect of dummy solder balls. It shows that the dummy solder balls are effective elements of the SSD to increase the fatigue life of an SSD by increasing 700 times of the fatigue life of the given SSD.