Solid State Drives Cache Research Articles

Cache eviction for SSD-HDD hybrid storage based on sequential packing

Hybrid storage systems consist of NAND flash-based solid-state drives (SSDs) and conventional hard disk drives (HDDs). In which, the SSD device commonly acts as a cache for HDDs, to not only reduce overall power consumption, but also enhance responsiveness of a system. Cache eviction, which is responsible for moving the data out of SSD during garbage collection (GC) so as to maximize the use efficiency of cache, may greatly affect normal I/O processing. This paper proposes a technique of sequential packing-based cache eviction, which groups the flushed data pages in the GC block, with the (cold) sequential address data located in other SSD blocks. Then, the packed data pages can be flushed together onto the lower level storage of HDDs, to fully use their high sequential bandwidth. As a result, our approach can cut down negative effects of write amplification on the SSD cache, and contribute to better I/O performance of SSD-HDD hybrid storage.

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.

Energy-Saving SSD Cache Management for Video Servers with Heterogeneous HDDs

Dynamic adaptive streaming over HTTP (DASH) technique, the most popular streaming method, requires a large number of hard disk drives (HDDs) to store multiple bitrate versions of many videos, consuming significant energy. A solid-state drive (SSD) can be used to cache popular videos, thus reducing HDD energy consumption by allowing I/O requests to be handled by an SSD, but this requires effective HDD power management due to limited SSD bandwidth. We propose a new SSD cache management scheme to minimize the energy consumption of a video storage system with heterogeneous HDDs. We first present a technique that caches files with the aim of saving more HDD energy as a result of I/O processing on an SSD. Based on this, we propose a new HDD power management algorithm with the goal of increasing the number of HDDs operated in low-power mode while reflecting the heterogeneous HDD power characteristics. For this purpose, it assigns a separate parameter value to each I/O task based on the ratio of HDD energy to bandwidth and greedily selects the I/O tasks handled by the SSD within limits on its bandwidth. Simulation results show that our scheme consumes between 12% and 25% less power than alternative schemes under the same HDD configuration.

ZoneTier

Integrating solid-state drives (SSDs) and host-aware shingled magnetic recording (HA-SMR) drives can potentially build a cost-effective high-performance storage system. However, existing SSD tiering and caching designs in such a hybrid system are not fully matched with the intrinsic properties of HA-SMR drives due to their lacking consideration of how to handle non-sequential writes (NSWs). We propose ZoneTier, a zone-based storage tiering and caching co-design, to effectively control all the NSWs by leveraging the host-aware property of HA-SMR drives. ZoneTier exploits real-time data layout of SMR zones to optimize zone placement, reshapes NSWs generated from zone demotions to SMR preferred sequential writes, and transforms the inevitable NSWs to cleaning-friendly write traffics for SMR zones. ZoneTier can be easily extended to match host-managed SMR drives using proactive cleaning policy. We implemented a prototype of ZoneTier with user space data management algorithms and real SSD and HA-SMR drives, which are manipulated by the functions provided by libzbc and libaio. Our experiments show that ZoneTier can reduce zone relocation overhead by 29.41% on average, shorten performance recovery time of HA-SMR drives from cleaning by up to 33.37%, and improve performance by up to 32.31% than existing hybrid storage designs.

ROCO: Using a Solid State Drive Cache to Improve the Performance of a Host-Aware Shingled Magnetic Recording Drive

Shingled magnetic recording (SMR) can effectively increase the capacity of hard disk drives (HDDs). Hostaware SMR (HA-SMR) is expected to be more popular than other SMR models because of its backward compatibility and new SMR-specific APIs. However, an HA-SMR drive often suffers performance degradation under write-intensive workloads because of frequent non-sequential writes buffered in the disk cache. The non-sequential writes mainly come from update writes, small random writes and out-of-order writes. In this paper, we propose a hybrid storage system called ROCO which aims to use a solid state drive (SSD) cache to improve the performance of an HA-SMR drive. ROCO reorders out-of-order writes belonging to the same zone and uses the SSD cache to absorb update writes and small random writes. We also design a data replacement algorithm called CREA for the SSD cache. CREA first conducts zone-oriented hot/cold data identification to identify cold-cached zones and hot-cached zones, and then evicts data blocks belonging to colder zones with higher priorities that can be sequentially written or written through host-side read-modify-write operations. It gives the lowest priority to data blocks belonging to the hottest-cached zone that have to be non-sequentially written. Experimental results show that ROCO can effectively reduce non-sequential writes to the HA-SMR drive and improve the performance of the HA-SMR drive.

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.

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.

Selective I/O Bypass and Load Balancing Method for Write-Through SSD Caching in Big Data Analytics

Fast network quality analysis in the telecom industry is an important method used to provide quality service. SK Telecom, based in South Korea, built a Hadoop-based analytical system consisting of a hundred nodes, each of which only contains hard disk drives (HDDs). Because the analysis process is a set of parallel I/O intensive jobs, adding solid state drives (SSDs) with appropriate settings is the most cost-efficient way to improve the performance, as shown in previous studies. Therefore, we decided to configure SSDs as a write-through cache instead of increasing the number of HDDs. To improve the cost-per-performance of the SSD cache, we introduced a selective I/O bypass (SIB) method, redirecting the automatically calculated number of read I/O requests from the SSD cache to idle HDDs when the SSDs are I/O over-saturated, which means the disk utilization is greater than 100 percent. To precisely calculate the disk utilization, we also introduced a combinational approach for SSDs because the current method used for HDDs cannot be applied to SSDs because of their internal parallelism. In our experiments, the proposed approach achieved a maximum 2x faster performance than other approaches.

SMRC: An Endurable SSD Cache for Host-Aware Shingled Magnetic Recording Drives

Host-aware shingled magnetic recording (HA-SMR) drives deliver higher disk capacity and expose internal zones to host, but their performance can be severely degraded when subjected to nonsequential writes (NSWs). Using solid state drives (SSDs) as a cache layer to proactively cache NSWs can alleviate this performance degradation. However, existing SSD caching designs are not capable of identifying and selectively caching NSWs, which incurs ineffectual SSD write traffic and excessively wears the limited write endurance of SSD. In this paper, we propose an endurable SMR-oriented SSD Caching (SMRC) framework. SMRC leverages the intrinsic host-aware property of HA-SMR drives to filter both sequential writes and innocuous NSWs out of SSD. Besides, SMRC reorganizes the cached NSWs to evict them with cleaning-friendly write traffic to HA-SMR drives. Our experimental results show that SMRC can effectively reduce SSD write traffic and improve system performance compared with existing SSD caching designs.

Improving Performance of Parallel I/O Systems through Selective and Layout-Aware SSD Cache

Parallel file systems (PFS) are widely-used to ease the I/O bottleneck of modern high-performance computing systems. However, PFSs do not work well for small requests, especially small random requests. Newer Solid State Drives (SSD) have excellent performance on small random data accesses, but also incur a high monetary cost. In this study, we propose SLA-Cache, a Selective and Layout-Aware Cache system that employs a small set of SSD-based file servers as a cache of conventional HDD-based file servers. SLA-Cache uses a novel scheme to identify performance-critical data, and conducts a selective cache admission (SCA) policy to fully utilize SSD-based file servers. Moreover, since data layout of the cache system can also largely influence its access performance, SLA-Cache applies a layout-aware cache placement scheme (LCP) to store data on SSD-based file servers. By storing data with an optimal layout requiring the lowest access cost among three typical layout candidates, LCP can further improve system performance. We have implemented SLA-Cache under the MPICH2 I/O library. Experimental results show that SLA-Cache can significantly improve I/O throughput, and is a promising approach for parallel applications.

A Hybrid Non-Volatile Cache Design for Solid-State Drives Using Comprehensive I/O Characterization

The emergence of new memory technologies provides us with opportunity to enhance the properties of existing memory architectures. One such technology is Phase Change Memory (PCM) which boasts superior scalability, power savings, non-volatility, and a performance competitive to Dynamic Random Access Memory (DRAM). In this paper, we propose a write buffer architecture for Solid-State Drives (SSDs) which attempts to exploit PCM as a DRAM alternative while alleviating its issues such as long write latency, high write energy, and finite endurance. To this end and based on thorough I/O characterization of desktop and enterprise applications, we propose a hybrid DRAM-PCM SSD cache design with an intelligent data movement scheme. This architecture manages to improve energy efficiency while enhancing performance and endurance. To study the design trade-offs between energy, performance, and endurance, we augmented Microsoft's DiskSim SSD model with a detailed hybrid cache using PCM and DRAM parameters from a rigorous survey of device prototypes. We study the design choices of implementing different PCM and DRAM arrays to achieve the best trade-off between energy and performance. The results display up to 77 percent power savings compared to a DRAM cache and up to percent reduction in request response time for a variety of workloads, while greatly improving disk endurance.

サーバSSDキャッシュと連携するストレージボリューム階層化方式の提案

For the system providing server SSD (Solid State Drive) cache and volume tiering functions, it is difficult to determine which data to be placed into SSD tier on the basis of the number of I/O (Input/Output). In this paper, we propose a volume tiering method cooperating with the server SSD cache, which counts the number of I/O on the server and allocates storage SSD to read-intensive areas. We conduct an I/O simulation experiment using I/O log traced on the real environment to validate the proposed method. We show that the proposed method reduces I/O average response time by up to 10% compared to the existing method through the I/O simulation experiment.

File-System-Level Storage Tiering for Faster Application Launches on Logical Hybrid Disks

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.

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.

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.

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.

Exploiting application-level similarity to improve SSD cache performance in Hadoop

To boost the performance of massive data processing, solid-state drives (SSDs) have been used as a kind of cache in the Hadoop system. However, most of existing SSD cache management algorithms are ...