Solid State Drives Research Articles

Probabilistic Page Replacement Policy in Buffer Cache Management for Flash-Based Cloud Databases

In the fast evolution of storage systems, the newly emerged flash memory-based Solid State Drives (SSDs) are becoming an important part of the computer storage hierarchy. Amongst the several advantages of flash-based SSDs, high read performance, and low power consumption are of primary importance. Amongst its few disadvantages, its asymmetric I/O latencies for read, write and erase operations are the most crucial for overall performance. In this paper, we proposed two novel probabilistic adaptive algorithms that compute the future probability of reference based on recency, frequency, and periodicity of past page references. The page replacement is performed by considering the probability of reference of cached pages as well as asymmetric read-write-erase properties of flash devices. The experimental results show that our proposed method is successful in minimizing the performance overheads of flash-based systems as well as in maintaining the good hit ratio. The results also justify the utility of a genetic algorithm in maximizing the overall performance gains.

Thermomechanical studies of surface mounted microelectronics by finite element analysis

Surface mounted microelectronic (SMT) consist of components and packages mounted on printed circuit board (PCB) via solder joints. The SMT assemblies are subjected to elevated temperature during processing as well as during usage cycles. The processes result in thermal-mechanical strain to the SMT package which can reduce the lifespan of the product. In this work, the thermomechanical behaviour of a surface mounted microelectronic package-a solid state drive (SSD) is studied by finite element analysis (FEA). The results show that bared PCB warped when it is heated while PCB with packages resists the warpage thus generating stress at the solder joints. The results show that the stress level at the top of the solder joint (between the package and solder ball) is higher than the stress generated at the bottom of the solder joint (between the solder ball and PCB). FEA simulation also shows when a package is reduced in size by 50%, the thermomechanical induced stress at the solder joints decreased to a value well below the stress limit of the solder joint materials.

Experimental Investigation of Frozen Solid State Drive on Digital Evidence with Static Forensic Methods

The rapid development of computer technology in hardware, is currently developing non-volatile computer storage media Solid State Drive (SSD). SSD technology has a faster data access speed than Hard Disk and is currently starting to replace Hard Disk storage media. Freezing software on computer systems is often carried out by computer technicians, because it can save a computer maintenance costs due to errors, be exposed to computer viruses or malware. This software is used to prevent unwanted changes to the computer system, when the computer is restarted changes that occur in the computer system will not be stored on storage media. When this happens, what should be done by digital forensic investigators. This study discusses experimental forensic investigations on SSD media storage with frozen conditions or in this study said the frozen SSD. Frozen SSD is the condition of the drive that is locked so that there is no change in the computer system. Software used to lock and prevent changes such as Deep Freeze, Shadow Defender, Windows Steady State, and Toolwiz Time Freeze. Forensic research stages using methods NIST. The result shows that from comparative analysis conducted with Deep Freeze the results of the RecoverMyFile gives 76.38% and Autopsy gives 75,27%, while frozen condition with Shadow Defender the results of the RecoverMyFile gives 59.72% and Autopsy gives 74.44%. So the results of this study indicate the drive freezing software has an effect obtained can be an obstacle in the digital forensic process.

Thermal Analysis of Ball Grid Array Non-Volatile Memory Express Solid-State Drive in Vacuum

Solid-state drives (SSDs) have been widely used in data storage for micro satellites, data centers, and mobile products due to their high performance and high reliability. Recently, Samsung Electronics starts to introduce a ball grid array (BGA) non-volatile memory express (NVMe) SSD, which is a combined package of controller and memory units (i.e., DRAM and NAND) into one package. Due to the smaller form factor with high storage densities and multi-functional devices of BGA NVMe SSDs, a thermal issue becomes more important, requiring fundamental understanding of heat dissipation mechanism. Moreover, for applications in micro satellites, thermal analysis of SSDs should be conducted in vacuum environment. Here, we investigate the heat dissipation mechanism in the newly developed BGA NVMe SSD in both the atmosphere and high-vacuum conditions. A finite-element-method-based numerical model is developed for the heat transfer analysis and is verified by experiments at various workload conditions.

Towards Adaptive Parallel Storage Systems

Disk I/O is a major bottleneck limiting the performance and scalability of data intensive applications. A common way to address disk I/O bottlenecks is using parallel storage systems and utilizing concurrent operation of independent storage components; however, achieving a consistently high parallel I/O performance is challenging due to static configurations. Modern parallel storage systems, especially in the cloud, enterprise data centers, and scientific clusters are commonly shared by various applications generating dynamic and coexisting data access patterns. Nonetheless, these systems generally utilize one-layout-fits-all data placement strategy frequently resulting in suboptimal I/O parallelism. Guided by association rule mining, graph coloring, bin packing, and network flow techniques, this paper proposes a general framework for adaptive parallel storage systems, with the goal of continuously providing a high-degree of I/O parallelism. Evaluation results indicate that the proposed framework is highly successful in adjusting to skewed parallel access patterns for both hard disk drive (HDD) based traditional storage arrays and solid-state drive (SSD) based all-flash arrays. In addition to the storage arrays, the proposed framework is sufficiently generic and can be tailored to various other parallel storage scenarios including but not limited to key-value stores, parallel/distributed file systems, and internal parallelism of SSDs.

Exploiting Internal Parallelism for Address Translation in Solid-State Drives

Solid-state Drives (SSDs) have changed the landscape of storage systems and present a promising storage solution for data-intensive applications due to their low latency, high bandwidth, and low power consumption compared to traditional hard disk drives. SSDs achieve these desirable characteristics using internal parallelism —parallel access to multiple internal flash memory chips—and a Flash Translation Layer (FTL) that determines where data are stored on those chips so that they do not wear out prematurely. However, current state-of-the-art cache-based FTLs like the Demand-based Flash Translation Layer (DFTL) do not allow IO schedulers to take full advantage of internal parallelism, because they impose a tight coupling between the logical-to-physical address translation and the data access. To address this limitation, we introduce a new FTL design called Parallel-DFTL that works with the DFTL to decouple address translation operations from data accesses. Parallel-DFTL separates address translation and data access operations into different queues, allowing the SSD to use concurrent flash accesses for both types of operations. We also present a Parallel-LRU cache replacement algorithm to improve the concurrency of address translation operations. To compare Parallel-DFTL against existing FTL approaches, we present a Parallel-DFTL performance model and compare its predictions against those for DFTL and an ideal page-mapping approach. We also implemented the Parallel-DFTL approach in an SSD simulator using real device parameters, and used trace-driven simulation to evaluate Parallel-DFTL’s efficacy. Our evaluation results show that Parallel-DFTL improved the overall performance by up to 32% for the real IO workloads we tested, and by up to two orders of magnitude with synthetic test workloads. We also found that Parallel-DFTL is able to achieve reasonable performance with a very small cache size and that it provides the best benefit for those workloads with large request size or with high write ratio.

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.

Optimizing the cost of DBaaS object placement in hybrid storage systems

In a Cloud context, Solid State Drive (SSD) became a must-have technology. This technology is too expensive to replace Hard Disk Drive (HDD), both are used in Hybrid Storage Systems (HSS). When it comes to storing data, placement strategies are employed to find the best storage class to use (SSD or HDD). While for many applications, those strategies need to be I/O performance driven, in a Cloud context, they must be cost driven: minimize the cost of data placement while satisfying Service Level Objectives. This paper presents two Cost based Object Placement Strategies (COPS) for DBaaS objects in HSS: a genetic algorithm based approach (G-COPS) and an ad-hoc heuristic approach (H-COPS) based on incremental optimizations. Both algorithms were tested for small and large instance problems. While G-COPS proved to be closer to the optimal solution in case of small instance problems, H-COPS showed a better scalability as it approached the exact solution even for large instance problems (by 10% on average). Both performed better than state-of-the-art solutions as they reduced the overall cost by more than 40%. In addition, H-COPS showed small execution times which makes it a good candidate for a runtime use. Moreover, H-COPS drastically limits the over-provisioning of resources.

Subpage-Aware Solid State Drive for Improving Lifetime and Performance

The manufacturers of NAND flash-based solid-state drives (SSDs) are increasing capacity and throughput by enlarging their page size, which is the minimum I/O unit in the NAND flash chips. Because the host and NAND flash chips have different I/O granularity units, the number of subpage requests increases. However, these subpage requests, especially writes, can cause internal fragmentation and endurance problems. Furthermore, subpage write requests inevitably involve read-modify-write (RMW) operations that increase the write response time because of the out-place-update feature in the NAND flash chips. In this paper, we propose a subpage-aware SSD to increase the lifetime and performance by reducing the number of NAND writes and eliminating unnecessary RMW operations. Our scheme attempts to merge subpage write requests to full page write requests in the write buffer to reduce the number of NAND writes and adds size information to the mapping table to detect unnecessary RMW operations. Our proposed scheme reduces the number of NAND writes by up to 30 and 19 percent on average and the write response time by up to 22 and 13 percent on average.

A generic and efficient framework for flash-aware spatial indexing

Spatial indexing on flash-based Solid State Drives (SSDs) has become a core aspect in spatial database applications, and has been carried out by flash-aware spatial indices. Although there are some flash-aware spatial indices proposed in the literature, they do not exploit all the benefits of SSDs, leading to loss of efficiency and durability. In this article, we propose eFIND, a new generic and efficient framework for flash-aware spatial indexing. eFIND takes into account the intrinsic characteristics of SSDs by employing (i) a write buffer to avoid expensive random writes, (ii) a flushing algorithm that smartly picks modifications to be flushed in batch to the SSD, (iii) a read buffer to decrease the overhead of random reads, (iv) a temporal control to avoid interleaved reads and writes, and (v) a log-structured approach to provide data durability. Performance tests showed the efficiency of eFIND. Compared to the state of the art, eFIND improved the construction of spatial indices from 43% to 77%, and the spatial query processing from 4% to 23%.

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.

Performance assessment of a software-based coincidence processor for the EXPLORER total-body PET scanner

Coincidence processing in positron emission tomography (PET) is typically done during acquisition of the data. However, on the EXPLORER total-body PET scanner we plan, in addition, to store unpaired single events (i.e. singles) for post-acquisition coincidence processing. A software-based coincidence processor was developed for EXPLORER and its performance was assessed. Our results showed that the performance of the coincidence processor could be significantly impacted by the type of data storage (Peripheral Component Interconnect Express (PCIe)-attached solid state drive (SSD) versus RAID 6 hard disk drives (HDDs)) especially when multiple data files were processed in parallel. We showed that a 48-thread computer node with dual Intel Xeon E5-2650 v4 central processing units (CPUs) and a PCIe SSD was sufficient to process approximately 120 M singles s−1 at an incoming singles rate of approximately 150 Mcps. With two computer nodes, near real-time coincidence processing became possible at this incoming singles rate.

Accelerating MapReduce on Commodity Clusters: An SSD-Empowered Approach

MapReduce, as a programming model and implementation for processing large data sets on clusters with hundreds or thousands of nodes, has gained wide adoption. In spite of the fact, we found that MapReduce on commodity clusters, which are usually equipped with limited memory and hard-disk drive (HDD) and have processors of multiple or many cores, does not scale as expected as the number of processor cores increases. The key reason for this is that the underlying low-speed HDD storage cannot meet the requirement of frequent IO operations. Though in-memory caching can improve IO, it is costly and sometimes cannot get the desired result either due to memory limitation. To deal with the problem and make MapReduce more scalable on commodity clusters, we present mpCache, a solution that utilizes solid-state drive (SSD) to cache input data and localized data of MapReduce tasks. In order to make a good trade-off between cost and performance, mpCache proposes ways to dynamically allocate the cache space between the input data and localized data and to do cache replacement. We have implemented mpCache in Hadoop and evaluated it on a 7-node commodity cluster by 13 benchmarks. The experimental results show that mpCache can gain an average speedup of 2.09 $\times$ when compared with Hadoop, and can achieve an average speedup of 1.79x when compared with PACMan, the latest in-memory optimization of MapReduce.

Lifespan Analysis for Redundant Array of Independent Module Based Solid State Drives

Solid state drives (SSD) are usually built using a NAND flash memory array. An SSD controller manages datastreams stripped to multiple flash memory channels. The device could be regarded as a level-0 structure of redundant array of independent disk (RAID), the technical term for a disk array system. This typical SSD controller architecture is called an intrinsic RAID (iRAID). An iRAID-based SSD controller has to execute sophisticated wear-leveling algorithms and dynamically calculate the lifespan of each flash memory chip as well as run error correction coding. redundant array of independent module (RAIM) was proposed to simplify the SSD controller by applying a group of standardized memory modules instead of raw flash memory chips. One of the widely recognized memory modules is the embedded multi-media card (eMMC) module, which is a small component, typically with a ball grid array package, and is found in consumer applications. In the paper, we have described the methods needed to optimize wear-leveling in an array of eMMC modules and deduced a formula to evaluate and optimize the lifespan of an RAIM-based SSD. A silicon SSD controller was implemented to verify the analysis. The positive result revealed that the lifespan of the RAIM-based SSD could be improved in consumer-grade eMMC modules.

ECI-Cache

In recent years, high interest in using Virtual Machines (VMs) in data centers and cloud computing has significantly increased the demand for high-performance data storage systems. A straightforward approach to providing a high-performance storage system is using Solid-State Drives (SSDs). Inclusion of SSDs in storage systems, however, imposes significantly higher cost compared to Hard Disk Drives (HDDs). Recent studies suggest using SSDs as a caching layer for HDD-based storage subsystems in virtualized platforms. Such studies neglect to address the endurance and cost of SSDs, which can significantly affect the efficiency of I/O caching. Moreover, previous studies only configure the cache size to provide the required performance level for each VM, while neglecting other important parameters such as cache write policy and request type, which can adversely affect both performance-per-cost and endurance. In this paper, we propose a new high-Endurance and Cost-efficient I/O caching (ECI-Cache) scheme for virtualized platforms in large-scale data centers, which improves both performance-per-cost and endurance of the SSD cache. ECI-Cache dynamically assigns 1) an efficient cache size for each VM, to maximize the overall performance of the running VMs and 2) an effective write policy for each VM, to enhance the endurance and performance-per-cost of the storage subsystem.

A high-performance and endurable SSD cache for parity-based RAID

Solid-state drives (SSDs) have been widely used as caching tier for disk-based RAID systems to speed up data-intensive applications. However, traditional cache schemes fail to effectively boost the parity-based RAID storage systems (e.g., RAID-5/6), which have poor random write performance due to the small-write problem. What’s worse, intensive cache writes can wear out the SSD quickly, which causes performance degradation and cost increment. In this article, we present the design and implementation of KDD, an efficient SSD-based caching system which Keeps Data and Deltas in SSD. When write requests hit in the cache, KDD dispatches the data to the RAID storage without updating the parity blocks to mitigate the small write penalty, and compactly stores the compressed deltas in SSD to reduce the cache write traffic while guaranteeing reliability in case of disk failures. In addition, KDD organizes the metadata partition on SSD as a circular log to make the cache persistent with low overhead. We evaluate the performance of KDD via both simulations and prototype implementations. Experimental results show that KDD effectively reduces the small write penalty while extending the lifetime of the SSD-based cache by up to 6.85 times.

System-level read disturb suppression techniques of TLC NAND flash memories for Read-Hot/Cold data mixed applications

In this paper, versatile triple-level cell (TLC) NAND flash memory control with four proposed techniques, Read-Hot/Cold Migration, Read Voltage Control (RVC), Edge Word-Line Protection (EWLP), and Worst Page Detection (WPD), is proposed for data center application solid-state drives (SSDs). To apply the optimal reliability enhancement techniques for stored data, first proposal of Read-Hot/Cold Migration separates read-hot/cold data into each region. Then, second proposal, Read Voltage Control applies the optimal read reference voltages (VREF) for each read-hot/cold region to improve the overall reliability of TLC NAND flash. Third proposal, Edge Word-Line Protection reduces the bit error rate (BER) of the edge word-lines (WLs), which have the worst reliability in read-hot data as reported in this paper. Finally, Worst Page Detection is proposed to predict the worst page BER in a block precisely to prevent judging entire block as bad and optimizes the refresh interval of read-hot block. By combining all of these techniques, the reliability of TLC NAND flash is enhanced for both read-hot and cold data.

Revisiting Immediate Duplicate Detection in External Memory Search

External memory search algorithms store the open and closed lists in secondary memory (e.g., hard disks) to augment limited internal memory. To minimize expensive random access in hard disks, these algorithms typically employ delayed duplicate detection (DDD), at the expense of processing more nodes than algorithms using immediate duplicate detection (IDD). Given the recent ubiquity of solid state drives (SSDs), we revisit the use of IDD in external memory search. We propose segmented compression, an improved IDD method that significantly reduces the number of false positive access into secondary memory. We show that A*-IDD, an external search variant of A* that uses segmented compression-based IDD, significantly improves upon previous open-addressing based IDD. We also show that A*-IDD can outperform DDD-based A* on some domains in domain-independent planning.

IO dependent SSD cache allocation for elastic Hadoop applications

Elastic Hadoop applications consisting of multiple virtual machines (VMs) are widely used to support big data analysis and processing. In this scenario, flash-based solid state drive (SSD) is usually deployed on hypervisors and used as the cache to improve the IO performance. However, existing SSD caching schemes are mostly VM-centric, which focus on the low-level IO performance metrics of individual VMs. They may not lead to the optimized performance of elastic Hadoop applications, i.e., the job completion time (JCT), as the importance of VMs inside the application are different even though they have the similar low-level IO patterns. Considering the IO dependency among VMs and figuring out the importance, which we regard as the application-centric metrics, may potentially better improve the performance. We present IO dependency based requirement model, to characterize the requirement of SSD cache for each VM inside the elastic Hadoop application, and then use it in a genetic algorithm (GA) based approach to calculate the nearly optimal weights of VMs for allocating the per-VM SSD cache space and the capacity of the I/O operations per second (IOPS). Furthermore, we present a tool AC-SSD based on the approach and introduce the closed-loop adaptation to react to continuously changing workloads. The evaluation shows that by using AC-SSD, the JCT is reduced by up to 39% for IO sensitive workloads, up to 29% for continuously changing workloads, and over 12.5% for different scale of data comparing to the shared cache.

ECI-Cache

In recent years, high interest in using Virtual Machines (VMs) in data centers and Cloud computing has significantly increased the demand for high-performance data storage systems. A straightforward approach to provide a high performance storage system is using Solid-State Drives (SSDs). Inclusion of SSDs in storage systems, however, imposes significantly higher cost compared to Hard Disk Drives (HDDs). Recent studies suggest using SSDs as a caching layer for HDD-based storage subsystems in virtualization platforms. Such studies neglect to address the endurance and cost of SSDs, which can significantly affect the efficiency of I/O caching. Moreover, previous studies only configure the cache size to provide the required performance level for each VM, while neglecting other important parameters such as cache write policy and request type, which can adversely affect both performance-per-cost and endurance. In this paper, we present a new high-Endurance and Cost-efficient I/O Caching (ECI-Cache) scheme for virtualized platforms, which can significantly improve both the performance-per-cost and endurance of storage subsystems as opposed to previously proposed I/O caching schemes. Unlike traditional I/O caching schemes which allocate cache size only based on reuse distance of accesses, we propose a new metric, Useful Reuse Distance (URD), which considers the request type in reuse distance calculation, resulting in improved performance-per-cost and endurance for the SSD cache. Via online characterization of workloads and using URD, ECI-Cache partitions the SSD cache across VMs and is able to dynamically adjust the cache size and write policy for each VM. To evaluate the proposed scheme, we have implemented ECI-Cache in an open source hypervisor, QEMU (version 2.8.0), on a server running the CentOS 7 operating system (kernel version 3.10.0-327). Experimental results show that our proposed scheme improves the performance, performance-per-cost, and endurance of the SSD cache by 17%, 30% and 65%, respectively, compared to the state-of-the-art dynamic cache partitioning scheme.