Non-volatile memory driver to drastically reduce input-output response time and maintain Linux device-mapper framework

Solid State Drives (SSDs) have become popular for mobile devices and PCs nowadays due to their advantages compared with conventional hard disk drives. However, they suffer from endurance problems. An effective approach to increase the endurance of an SSD is to improve the management algorithm of the RAM buffer, a cache-like structure inside the SSD. There are several previous studies of RAM buffer management algorithms which improve the I/O performance and endurance of SSDs by reordering the write operations and exploiting the temporal locality of the write stream. We observe that the sequential write patterns of the write stream can be exploited to reduce the number of expensive erase operations. Using this insight, we propose a new effective method for RAM buffer management algorithms to enhance the endurance of SSDs. We evaluate our proposed method by software simulation and the result shows that the proposed method reduces the number of erasures and average response time compared to the best existing method by up to 17.3% and 16.9% respectively.

Read disturb is a circuit-level noise in high-density solid-state drives (SSDs), which may corrupt existing data in SSD blocks and then cause high read error rates. The approach of read reclaim (RR) is commonly used to avoid read disturb errors by migrating the valid data pages to other free blocks for resetting negative effects of read disturbs, but it affects both I/O responsiveness and SSD lifetime. This paper proposes SWEEP to minimize the number of RR operations. Specifically, it gathers hot read and hot write data pages together, and saves them in a portion of designated blocks. After that, the hot write data pages will be invalidated and the hot read data pages in such blocks are likely to be migrated to other blocks through garbage collection (GC). Consequently, the read disturbs on these hot read data pages will be reset, without additional RR operations. Trace-driven simulation experiments show that our proposal can significantly reduce the number of RR operations by between 1.6 % and 74.4 %, which contributes to a maximum reduction of 29.7 % on total erase operations, indicating a better lifetime of SSD devices. In addition, it can reduce the overall I/O response time by up to 52.9 %, compared to existing optimization schemes for SSDs.

Recently, flash-based solid state drives (SSDs) have become a primary storage solution due to their advantages over hard-disk drives. Nonetheless, SSD management presents unique challenges. First, SSDs update data by writing a new copy to a clean slot, rather than overwriting old data. Second, SSDs support cleaning of entire blocks, while slots cannot be cleaned individually. Third, when cleaning a block, any valid data it stores must first be rewritten to another location, which adversely affects the SSD endurance and throughput. In this work, we address the SSD management problem, where the objective is to minimize the number of rewrites in SSDs. We approach the problem from a theoretical perspective, analyzing algorithms in a worst-case fashion. Motivated by recent advances in machine learning, we consider a learning-augmented setting where the algorithm has access to a predictive oracle, with performance guarantees expressed as a function of the error in the oracle's output. Our main contribution is Gladiator, a novel online algorithm that optimally leverages predictions to enhance the performance of SSDs. Compared to prior work, Gladiator requires no additional non-volatile memory and is less sensitive to prediction errors. We further demonstrate the effectiveness of Gladiator under reasonable assumptions on the input distribution, and extend it to an extremely efficient offline algorithm. Empirical results confirm its superiority over state-of-the-art practical solutions across diverse SSD workloads.

Solid state drives (SSDs) are increasingly deployed to construct storage arrays (RAIDs) in enterprise environments. The design decisions in RAID are traditionally devised for HDD RAIDs, which cannot fully exploit the characteristics of SSDs. In particular, SSD lacks the ability to update pages in-place. Random writes in traditional parity-based RAIS (SSD RAID) systems that has static striping result in significantly more writes, degraded performance, and shortened SSD lifetime. By dynamically forming full stripes, log-based design originally proposed in HDD RAID can mitigate the write-hole problem caused by random writes. However, it needs a directory to record locations for all data blocks, resulting in large space overhead and consequently sacrificing addressing efficiency. In this paper, we propose CD-RAIS, a compromise between static striping and dynamic striping. CD-RAIS groups requests that are from different SSD drives and places their corresponding unnecessarily consecutive logical blocks in one stripe. It mitigates the write-hole problem, meanwhile remains the same addressing efficiency as static striping. To enable dynamic data striping fit SSDs, CD-RAIS performs lazy data invalidation and consolidates updates to parity blocks. CD-RAIS greatly alleviates the write request increase due to parity block update in RAIS. Our experimental results show that, for random write dominated workloads, CD-RAIS achieves 65% response time improvement and 31% longer SSD lifespan over traditional RAIS schemes.

Non-volatile random access memory (NVRAM) is a promising approach to persistent data storage with outstanding advantages over traditional storage devices, such as hard disk drives (HDDs) and solid state drives (SSDs). Some of its biggest advantages are its DRAM-like read latency and microsecond-level write latency, which are several hundred times faster than those in the original block device. However, one of the issues with using NVRAM as a storage device is designing an indexing system for its data stores to fully utilize NVRAM characteristics. The state-of-the-art indexing systems of non-volatile key-value stores are usually based on B+-trees or their variants, which were originally designed for block-based storage devices with better sequential performance than random performance. The semantics of B+-tree require data being sorted into leaf nodes and inner nodes and frequent splitting and merging to keep balanced. However, all the sorting, splitting, and merging operations cause extra write to NVRAM, which decreases its performance. In this article, we propose NV-Skiplist, a skiplist-based indexing system for key-value stores on NVRAM that fully uses the features of both NVRAM and DRAM. NV-Skiplist constructs its bottom layer in non-volatile memory to maintain data persistence and support range scans. It builds its upper layers in DRAM to retain rapid index searching and prevent consistently large overhead. We also propose a multiranged variant of NV-Skiplist to increase its search performance and scalability. We evaluate the performance of NV-Skiplist and wB+-tree which is a state-of-art scheme on an NVRAM emulator on a server with an Intel Xeon E5-2620 v2 processor. The results show that our design outperforms the original tree-based, non-volatile key-value stores up to 48%.

Solid-state drives (SSDs) are widely used in storage systems. However, algorithms adopted by existing operating systems generally consider the underlying devices as hard disks, and thus are rarely optimized for SSDs. In this paper, we focus on a classical research issue, the cache replacement policy, and design a new policy by taking the parallelism of SSDs into account.A typical SSD contains several parallel channels. Some channels contain more hot data, thus are busy with read requests. The other channels may only contain cold data. So, workloads among these channels may be unbalanced. Requests issued to busy channels may take a while to address, whereas requests issued to idle channels may be served rapidly. We design a new cache replacement policy for read data, which considers the unbalanced workloads among channels. The policy gives higher priority to evicting pages from idle channels because they are more easily retrieved. On the other hand, pages from busy channels are protected. In this manner, the average latency for obtaining pages is reduced. However, SSDs are black boxes, with operating systems are blind to the channel from which a page comes. Therefore, we propose a simple scheme that determines whether a page is from a busy or an idle channel. The scheme monitor the page requests issued to the underlying storage device. When a page request is returned, and many page requests issued earlier than it have not been returned, the page is assumed to be from an idle channel. We compare our cache replacement policy with others via trace-driven simulations. The performance is measured in terms of average response time. Comparison candidates include LRU, ARC and the policy adopted by Linux. The experimental results show that our policy outperforms the other policies on most traces when workloads among channels are unbalanced.

As data volume grows rapidly and queries become more complex, database engine has to deal with larger amount of temporary data when performing operators such as sort and join. Generally, these temporary data are organized as small temporary files stored in HDD. Due to the poor access performance on HDD, processing time of I/O operations on these files has a direct impact on the response time of queries. Compared to HDD, solid-state drive (SSD) offers more random IOPS and comparable sequential bandwidth. So, using SSD to replace HDD may be an ideal pattern when dealing with temporary data. In this paper, we find out that query processing performing improvement is unsatisfactory if we store temporary files in SSD directly. Thus, we propose a SSD aware temporary data management policy called STDM. STDM takes both the I/O behaviors of temporary data and SSD physical characteristics into account. Temporary data are cached in memory at first, and then written to SSD in an append-only fashion. In this way, we reduce random writes and take full advantage of fast random reads on SSD. We implement a prototype based on PostgreSQL and evaluate its performance on TPC-H benchmark. Experiment result shows that STDM improves query processing performance dramatically compared to the traditional method for organizing temporary data.

Application launch times can be reduced by using low-capacity solid-state drives (SSDs) in combination with the conventional magnetic hard disk drives (HDDs). We propose a storage tiering scheme for a logically integrated hybrid disk that eliminates the requirement to store a block-mapping table on the SSDs, as well as the need to access this table for every I/O: these characteristics distinguish our scheme from most other SSD caching and driver-level tiering schemes. A heuristic identifies the disk blocks accessed during application launches, which are then moved from HDDs to SSDs by updating file-system pointers. In experiments with eight widely used applications, launch times were reduced by an average of 56 %, and our scheme outperformed the Linux DM-Cache by 6.5% on average. Because it runs as a background process, the block migration overhead is negligible. We also propose edge-block first and shortest sequence and long seek first (SSLSF) strategies for using limited SSD space effectively. Small collections of blocks, which have to be dispatched to the device driver together, are preferentially moved to the SSD especially when those blocks incur long seek times on the HDD. This produces a large reduction in HDD seek time for a small outlay of space on the SSD. In a scenario in which only 10% of launch blocks can be migrated to the SSD, the SSLSF strategy achieves average launch times for Eclipse and LibreOffice, which are 5.8% less than those exhibited by the widely used strategy of migrating short sequences of blocks first.

Statistical modeling of emergency medical services’ response and rescue times to road traffic crashes in the Kingdom of Saudi Arabia

Non-volatile memories (NVM) including flash memories and resistive memories have attracted significant interest as data storage media. Flash memories are widely employed in mobile devices and solid-state drives (SSD). Resistive memories are promising as storage class memory and embedded memory applications. Data reliability is the fundamental requirement of NVM as data storage media. However, modern nano-scale NVM suffers from challenges of inter-cell interference (ICI), charge leakage, and write endurance, which threaten the reliability of stored data. In order to cope with these adverse effects, advanced coding techniques including soft decision decoding have been investigated actively. However, current coding techniques do not capture the physical properties of NVM well, so the improvement of data reliability is limited. Although soft decision decoding improves the data reliability by using soft decision values, it degrades read speed performance due to multiple read operations needed to obtain soft decision values. In this dissertation, we explore coding schemes that use side information corresponding to the physical phenomena to improve the data reliability significantly. The side information is obtained before writing data into memory and incorporated during the encoding stage. Hence, the proposed coding schemes maintain the read speed whereas the write speed performance would be degraded. It is a big advantage from the perspective of speed performance since the read speed is more critical than the write speed in many memory applications. First, this dissertation investigates the coding techniques for memory with stuckat defects. The idea of coding techniques for memory with stuck-at defects is employed to handle critical problems of flash memories and resistive memories. For 2D planar flash memories, we propose a coding scheme that combats the ICI, which is a primary challenge of 2D planar flash memories. Also, we propose a coding scheme that reduces the effect of fast detrapping, a degradation factor in 3D vertical flash memories. Finally, we investigate the coding techniques that improve write endurance and power consumption of resistive memories.

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.

Solid-state drives (SSDs) have recently become a common storage component in computer systems, and they are fueled by continued bit cost reductions achieved with smaller feature sizes and multiple-level cell technologies. However, as the flash memory stores more bits per cell, the performance and reliability of the flash memory degrade substantially. To solve this problem, a fast non-volatile memory (NVM-)based cache has been employed within SSDs to reduce the long latency required to write data. Absorbing small writes in a fast NVM cache can also reduce the number of flash memory erase operations. To maximize the benefits of an NVM cache, it is important to increase the NVM cache utilization. In this paper, we propose and study ProCache, a simple NVM cache management scheme, that makes cache-entrance decisions based on random probability testing. Our scheme is motivated by the observation that frequently written hot data will eventually enter the cache with a high probability, and that infrequently accessed cold data will not enter the cache easily. Owing to its simplicity, ProCache is easy to implement at a substantially smaller cost than similar previously studied techniques. We evaluate ProCache and conclude that it achieves comparable performance compared to a more complex reference counter-based cache-management scheme.

Flash memory based solid state drives (SSD) are increasingly common in portable and mobile computing devices such as laptops, mobile phones, and tablets. Due to space, weight, and power constraints, portable devices are often restricted to a single storage device, which makes them susceptible to data loss from internal errors. On the other hand, mission-critical mobile applications like wireless healthcare always demand a high level of data reliability. This is mainly because data sampled from mobile and dynamic environments are most likely irreproducible. An effective approach to improving storage and data reliability is the RAID (redundant arrays of inexpensive disks) organization. However, the multiple disks required to implement RAID make it incompatible with the aforementioned restrictions of many portable devices. In this paper, we propose a SIRF (single internally redundant flash) architecture that leverages the internal hierarchical structure and parallelism of SSDs to provide redundancy similar to RAID in a single drive configuration. The initial effort focuses on implementing SIRF-1 (mirroring), which is the corollary to its RAID-1 counterpart. In SIRF-1, data is mirrored across SSD channels to optimally exploit parallelism for both read and write operations. Simulation results show that for read-dominant workloads SIRF-1 significantly outperforms a non-mirrored SSD by up to 39.5% in terms of mean response time. For write-intensive workloads, SIRF-1 pays a performance penalty no more than 5.5%.

Driver Response Time of Queuing Vehicles at Urban Signalized Intersections

When applications require high I/O performance, solid-state drives (SSDs) are often preferable because they perform better than traditional hard-disk drives (HDDs). Therefore, database system response time can be improved by moving frequently used data to SSDs. However, because capacity is both more limited and more expensive for SSDs than it is for HDDs, there is a need to determine which data are most appropriate to migrate from HDDs to SSDs. In this paper, we propose a priority-based data placement method for databases using SSDs by considering an integrated mechanism and a migration rule for performing migrations between HDDs and SSDs. The proposed method can provide database systems with high I/O performance by properly moving high-priority data to SSDs with fast access capabilities while maintaining the low-priority data on HDDs, which have lower costs. The results of experiments show that the proposed method can achieve the goal while requiring only small amounts of SSD capacity.