Endurable SSD-Based Read Cache for Improving the Performance of Selective Restore from Deduplication Systems

Deduplication 通常在两个企业存储系统和云存储被使用了。克服性能挑战为选择恢复 deduplication 系统的操作, solid-state-drive-based (即,基于 SSD ) 读的缓存能为由缓冲加快被部署流行动态地恢复内容。不幸地,经常的数据更改由古典缓存计划导致了(例如, LRU 和 LFU ) 显著地弄短 SSD 一生当在 SSD 减慢 I/O 进程时。处理这个问题,我们建议新解决方案砍缓存极大地由扩大比例象 I/O 性能一样改进 SSD 的 write 耐久性长期流行(砍) 在写进基于 SSD 的缓存的数据之中的数据。砍缓存保留很长时间在 SSD 缓存砍数据减少的时期缓存代替的数字。而且,它在 deduplication 集装箱阻止不得人心或不必要的数据被写进 SSD 缓存。我们在一个原型 deduplication 系统实现了砍缓存评估它的性能。我们的试验性的结果显示砍缓存弄短潜伏选择与仅仅 deduplicated 数据的 5.56% 能力以小基于 SSD 的缓存的成本由 37.3% 的一般水准恢复。重要地,砍缓存由 9.77 的一个因素改进 SSD 一生。砍缓存为一个成本效率的基于 SSD 的读的缓存解决方案提供到的证据表演增加性能选择为 deduplication 恢复系统。     

MacroTrend: A Write-Efficient Cache Algorithm for NVM-Based Read Cache

The future storage systems are expected to contain a wide variety of storage media and layers due to the rapid development of NVM(non-volatile memory)techniques.For NVM-based read caches,many kinds of NVM devices cannot stand frequent data updates due to limited write endurance or high energy consumption of writing.However,traditional cache algorithms have to update cached blocks frequently because it is difficult for them to predict long-term popularity according to such limited information about data blocks,such as only a single value or a queue that reflects frequency or recency.In this paper,we propose a new MacroTrend(macroscopic trend)prediction method to discover long-term hot blocks through blocks'macro trends illustrated by their access count histograms.And then a new cache replacement algorithm is designed based on the MacroTrend prediction to greatly reduce the write amount while improving the hit ratio.We conduct extensive experiments driven by a series of real-world traces and find that compared with LRU,MacroTrend can reduce the write amounts of NVM cache devices significantly with similar hit ratios,leading to longer NVM lifetime or less energy consumption.     

Leach: an automatic learning cache for inline primary deduplication system

Deduplication technology has been increasingly used to reduce storage costs. Though it has been successfully applied to backup and archival systems, existing techniques can hardly be deployed in primary storage systems due to the associated latency cost of detecting duplicated data, where every unit has to be checked against a substantially large fingerprint index before it is written. In this paper we introduce Leach, for inline primary storage, a self-learning in-memory fingerprints cache to reduce the writing cost in deduplication system. Leach is motivated by the characteristics of real-world I/O workloads: highly data skew exist in the access patterns of duplicated data. Leach adopts a splay tree to organize the on-disk fingerprint index, automatically learns the access patterns and maintains hot working sets in cachememory, with a goal to service a majority of duplicated data detection. Leveraging the working set property, Leach provides optimization to reduce the cost of splay operations on the fingerprint index and cache updates. In comprehensive experiments on several real-world datasets, Leach outperforms conventional LRU (least recently used) cache policy by reducing the number of cache misses, and significantly improves write performance without great impact to cache hits.     

A fully persistent and consistent read/write cache using flash-based general SSDs for desktop workloads

The flash-based SSD is used as a tiered cache between RAM and HDD. Conventional schemes do not utilize the nonvolatile feature of SSD and cannot cache write requests. Writes are a significant, or often dominant, fraction of storage workloads. To cache write requests, the SSD cache should persistently and consistently manage its data and metadata, and guarantee no data loss even after a crash. Persistent cache management may require frequent metadata changes and causes high overhead. Some researchers insist that a nonvolatile persistent cache requires new additional primitives that are not supported by general SSDs in the market. We proposed a fully persistent read/write cache, which improves both read and write performance, does not require any special primitive, has a low overhead, guarantees the integrity of the cache metadata and the consistency of the cached data, even during a crash or power failure, and is able to recover the flash cache quickly without any data loss. We implemented the persistent read/write cache as a block device driver in Linux. Our scheme aims at virtual desktop infra servers. So the evaluation was performed with massive, real desktop traces of five users for ten days. The evaluation shows that our scheme outperforms an LRU version of SSD cache by 50% and the read-only version of our scheme by 37%, on average, for all experiments. This paper describes most of the parts of our scheme in detail. Detailed pseudo-codes are included in the Appendix.     

支持高并发访问的新型NVM存储系统

I/O系统软件栈是影响NVM存储系统性能的重要因素。针对NVM存储系统的读写速度不均衡、写寿命有限等问题，设计了同异步融合的访问请求管理策略；在使用异步策略管理数据量较大的写操作的同时，仍然使用同步策略管理读请求和少量数据的写请求。针对多核处理器环境下不同计算核心访问存储系统时地址转换开销大的问题，设计了面向多核处理器地址转换缓存策略，减少地址转换的时间开销。最后实现了支持高并发访问NVM存储系统（CNVMS）的原型，并使用通用测试工具进行了随机读写、顺序读写、混合读写和实际应用负载的测试。实验结果表明，与PMBD相比，所提策略能提高1%~22%的读写速度和9%~15%的IOPS，验证了CNVMS策略能有效提高NVM存储系统的I/O性能和访问请求处理速度。     

An effective pre-store/pre-load method exploiting intra-request idle time of NAND flash-based storage devices

NAND flash-based storage devices (NFSDs) are widely employed owing to their superior characteristics when compared to hard disk drives. However, NAND flash memory (NFM) still exhibits drawbacks, such as a limited lifetime and an erase-before-write requirement. Along with effective software management, the implementation of a cache buffer is one of the most common solutions to overcome these limitations. However, the read/write performance becomes saturated primarily because the eviction overhead caused by limited DRAM capacity significantly impacts overall NFSD performance. This paper therefore proposes a method that hides the eviction overhead and overcomes the saturation of the read/write performance. The proposed method exploits the new intra-request idle time (IRIT) in NFSD and employs a new data management scheme. In addition, the new pre-store eviction scheme stores dirty page data in the cache to NFMs in advance. This reduces the eviction overhead by maintaining a sufficient number of clean pages in the cache. Further, the new pre-load insertion scheme improves the read performance by frequently loading data that needs to be read into the cache in advance. Unlike previous methods with large migration overhead, our scheme does not cause any eviction/insertion overhead because it actually exploits the IRIT to its advantage. We verified the effectiveness of our method, by integrating it into two cache management strategies which were then compared. Our proposed method reduced read latency by 43% in read-intensive traces, reduced write latency by 40% in write-intensive traces, and reduced read/write latency by 21% and 20%, respectively, on average compared to NFSD with a conventional write cache buffer.     

基于虚拟存储层的缓存控制软件设计

缓存加速技术可以利用固态硬盘(SSD,solid state disk)随机访问性能高的优势,提升机械硬盘的随机读写性能;传统的缓存加速技术难以适应大数据背景下高并发、间歇性频繁访问等热点数据访问需求;为了提升缓存整体性能,提出一种基于虚拟存储层的缓存策略(CVSL,cache policy based on the virtual storage layer),将缓存技术和分层存储技术相结合,通过热度统计、数据逻辑迁移,实现基于数据逻辑分层的缓存控制;实验结果表明,相对传统的缓存策略,CVSL策略的随机读写性能提升了9％～10％,未见明显波动,在缓存命中率方面具有良好的效果,达到了预期设计目标.     

应用透明的超算多层存储加速技术研究

在E级计算时代,超算系统一般使用多层存储架构以满足应用数据访问的容量和性能需求,这种架构中不同层次的存储介质差异较大,难以实现统一名字空间管理,往往需要应用修改数据访问流程才能最大程度利用到多层存储的性能和容量优势。针对多层存储统一名字空间的问题,提出针对非易失性双列存储模块（NVDIMM）的块级缓存和针对突发缓冲存储（BB）的文件级缓存技术。基于NVDIMM的块级缓存技术对缓存窗口灵活控制,以支持数据块粒度的异步读写,实现NVDIMM与BB层统一名字空间管理;基于BB的文件级缓存技术将数据缓存在BB层中,并动态迁移和管理文件副本,实现BB层与传统磁盘文件系统统一名字空间管理。在神威E级原型验证系统中的测试结果表明,所提出的两种技术较好地解决了多层存储的透明加速难题,NVDIMM块级缓存与BB相比,在缓存窗口16 MB时128 KB顺序读写带宽分别提升27%和36%,8 KB随机读写带宽分别提升20%和37%;基于BB的文件缓存技术利用BB的高带宽支撑数据访问,与全局文件系统相比,128 KB顺序读写带宽分别提升55%和141%,8 KB随机读写带宽分别提升163%和209%。此外,实际应用的测试也表明以上两种缓存技术具有透明的存储加速效果。     

面向费用优化的云存储缓存策略

为提高云存储的访问速率并降低费用,提出了一种面向费用优化的云存储缓存策略。利用几乎免费的局域网环境下的多台桌面计算机,在本地建立一个分布式文件系统,并将其作为远端云存储的缓存。进行文件读取时,首先查找其是否在缓存中,若存在则直接从缓存读取;若不存在则从远端云存储读取。采用了最近最少使用(LRU)算法进行缓存替换,将冷门数据从缓存中替换掉。以亚马逊简单存储服务(S3)作为远端的云存储服务,对原型系统进行了简单的性能测试。测试结果表明,使用了所提出的缓存策略后,在降低费用的同时能够显著提高文件读取的速度。     

海量小文件系统的可移植操作系统接口兼容技术

基于Hadoop分布式文件系统（HDFS）研发的海量小文件系统（SMDFS）遗留了HDFS不兼容可移植操作系统接口（POSIX）约束的问题,为解决SMDFS的这一问题,提出基于本地缓存的POSIX兼容技术和基于数据暂存区的元数据高效管理技术。首先,通过设置数据暂存区来实现读写模式文件流的重定向,然后建立异步线程池模型,实现数据暂存区镜像文件的同步,从而完成用户层到存储层的所有POSIX相关的文件操作。此外,借助跳表结构的元数据缓存实现List目录等元数据操作效率优化。测试表明,相较于HDFS的Linux客户端,基于技术成果实现的SMDFS3.0的随机读性能有10倍以上的性能提升,顺序读和顺序写性能有约3~4倍的提升,随机写性能可以达到本地文件系统的20%,基于目录的元数据缓存的设计使目录的List操作效率提升近10倍。但是,由于用户空间文件系统（FUSE）挂载的客户端会引入额外的内核态和用户态切换等带来的开销,因此SMDFS3.0的Linux客户端相对于系统的Java接口会有大约50%的性能损耗。     

Improving Metadata Caching Efficiency for Data Deduplication via In-RAM Metadata Utilization

We describe a data deduplication system for backup storage of PC disk images, named in-RAM metadata utilizing deduplication (IR-MUD). In-RAM hash granularity adaptation and miniLZO based data compression are firstly proposed to reduce the in-RAM metadata size and thereby reduce the space overheads required by the in-RAM metadata caches. Secondly, an in-RAM metadata write cache, as opposed to the traditional metadata read cache, is proposed for further reducing metadata-related disk I/O operations and improving deduplication throughput. During deduplication, the metadata write cache is managed following the LRU caching policy. For each manifest that is hit in the metadata write cache, an expensive manifest reloading operation from the disk is avoided. After deduplication, all the manifests in the metadata write cache are cleared and stored on the disk. Our experimental results using 1.5 TB real-world disk image dataset show that 1) IR-MUD achieved about 95% size reduction for the deduplication metadata, with a small time overhead introduced, 2) when the metadata write cache was not utilized, with the same RAM space size for the metadata read cache, IR-MUD achieved a 400% higher RAM hit ratio and a 50% higher deduplication throughput, as compared with the classic Sparse Indexing deduplication system where no metadata utilization approaches are utilized, and 3) when the metadata write cache was utilized and enough RAM space was available, IR-MUD achieved a 500% higher RAM hit ratio compared with Sparse Indexing and a 70% higher deduplication throughput compared with IR-MUD with only a single metadata read cache. The in-RAM metadata harnessing and metadata write caching approaches of IR-MUD can be applied in most parallel deduplication systems for improving metadata caching efficiency.     

基于LSM-tree的键值存储系统的读写性能优化

在写密集型工作环境中,日志结构合并树(log-structured-merge,LSM-Tree)已逐渐成为的主流存储系统,LSM-tree存在读操作速度慢、写操作成本高、范围查询操作效率低等问题;针对这些问题,为提升LSM-tree的性能进行了研究,提出了一种基于LSM-tree的键值存储系统的读写性能优化策略,通过键值分离策略设计vTree结构,并提出层内归并与消极的层间合并相结合的方法,以及范围查询优化合并的策略,从而优化系统的范围查询性能,在LSM-tree和vTree采用不同的压缩结构,以实现系统读写性能的提升;实验结果表明,与RocksDB相比读性能提升30%,与RocksDB-vTree相比范围查询性能提升10%。     

相似性-局部性方法相关参数分析

大数据时代到来,备份数据量增大给存储空间带来新的挑战。重复数据删除技术在备份存储系统中正逐渐流行,但大量数据访问,造成了磁盘的很大负担。针对重复数据删除技术存在的块索引查询磁盘瓶颈问题,文中提出了文件相似性与数据流局部性结合方法改善磁盘I/O性能。该方法充分发挥了各自的优势,相似性优化了索引查找,可以检测到相同数据检测技术不能识别的重复数据;而数据局部性保留了数据流的序列,使得cache的命中率提高,减少磁盘访问次数。布鲁过滤器存储数据块索引可节省大量查询时间和空间开销。对于提出的解决方法所涉及的重要参数如块大小、段大小以及对误判率的影响做了深入分析。通过相关实验评估与性能分析,实验数据与结果为进一步系统性能优化问题提供了重要的数据依据。     

ACAWM：一种NAS高性能协作写缓存机制

1.引言随着计算机技术的不断发展,对更大数据存储容量和更快信息访问速度的要求也越来越高。为了满足这样的需求,一种新型数据存储技术——附网存储(NAS,Net AttachedStorage)出现了。NAS是一种不依赖于平台的高性能数据存储技术,它使用专门优化的硬件和软件来提供高性能的文件服务。NAS的优点在于:①不依赖于平台的多协议数据存储     

基于非易失性存储器的存储引擎性能优化

非易失性存储器具有接近内存的读写速度,可利用其替换传统的存储设备,从而提升存储引擎的性能。但是,传统的存储引擎通常使用通用块接口读写数据,导致了较长的 I/O 软件栈,增加了软件层的读写延迟,进而限制了非易失性存储器的性能优势。针对这一问题,该文以 Ceph 大数据存储系统为基础,研究设计了基于非易失性存储器的新型存储引擎 NVMStore,通过内存映射的方式访问存储设备,根据非易失性存储器的字节可寻址和数据持久化特性,优化数据读写流程,从而减小数据写放大以及软件栈的开销。实验结果表明,与使用非易失性存储器的传统存储引擎相比,NVMStore能够显著提升 Ceph 的小块数据读写性能。     

Next High Performance and Low Power Flash Memory Package Structure

In general, NAND flash memory has advantages in low power consumption, storage capacity, and fast erase/write performance in contrast to NOR flash. But, main drawback of the NAND flash memory is the slow access time for random read operations. Therefore, we proposed the new NAND flash memory package for overcoming this major drawback. We present a high performance and low power NAND flash memory system with a dual cache memory. The proposed NAND flash package consists of two parts, i.e., an NAND flash memory module, and a dual cache module. The new NAND flash memory system can achieve dramatically higher performance and lower power consumption compared with any conventionM NAND-type flash memory module. Our results show that the proposed system can reduce about 78% of write operations into the flash memory cell and about 70% of read operations from the flash memory cell by using only additional 3KB cache space. This value represents high potential to achieve low power consumption and high performance gain.     

Exploiting flash memory characteristics to improve performance of RAIS storage systems

Redundant array of independent SSDs (RAIS) is generally based on the traditional RAID design and implementation. The random small write problem is a serious challenge of RAIS. Random small writes in parity-based RAIS systems generate significantly more pre-reads and writes which can degrade RAIS performance and shorten SSD lifetime. In order to overcome the well-known write-penalty problem in the parity-based RAID5 storage systems, several logging techniques such as Parity Logging and Data Logging have been put forward. However, these techniques are originally based on mechanical characteristics of the HDDs, which ignore the properties of the flash memory. In this article, we firstly propose RAISL, a flash-aware logging method that improves the small write performance of RAIS storage systems. RAISL writes new data instead of new data and pre-read data to the log SSD by making full use of the invalid pages on the SSD of RAIS. RAISL does not need to perform the pre-read operations so that the original characteristics of workloads are kept. Secondly, we propose AGCRL on the basis of RAISL to further boost performance. AGCRL combines RAISL with access characteristic to guide read and write cost regulation to improve the performance of RAIS storage systems. Our experiments demonstrate that the RAISL significantly improves write performance and AGCRL improves both of write performance and read performance. AGCRL on average outperforms RAIS5 and RAISL by 39.15% and 16.59% respectively.     

Deconstructing on-board disk cache by using block-level real traces

On-board disk cache is an effective approach to improve disk performance by reducing the number of physical accesses to the magnetic media. Disk drive manufacturers are increasing the on-board disk cache size to match the capacity growth of the backend magnetic media. Some disk drives nowadays have a cache of 32 MB. Modern computer systems use large amounts of memory to improve performance, any data brought into host memory will be re-accessed there, not in the on-board disk cache. This feature has a significant impact on the behavior of disk cache. This is because computer systems are complex systems consisting of various components. The components are correlated with each other. Therefore, a specific component cannot be isolated from the overall system when we analyze its performance behavior. This paper employs four block-level real traces to explore the performance behavior of the on-board disk cache by considering the impacts of the cache hierarchy contained in computer systems. The analysis gives three major implications: (1) I/O stream at block-level contains negligible temporal locality. Therefore, read/write cache can only achieve marginal benefits. (2) Static write cache does not achieve performance gains since the write stream does not have too much interference with the read stream. Therefore, it is better to leave the on-board disk cache shared by both the write and read streams. (3) Read cache dominates the contribution to the hit ratio besides prefetch. Thus, it is better to focus on improving the read performance rather than write performance of disk cache.     

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.     

支持Hadoop大数据访问的pNFS框架研究与实现*

随着大数据时代到来,分布式文件系统支持Hadoop大数据访问已成为一种趋势。本文以研究支持Hadoop大数据访问的pNFS框架为目的,采用在Hadoop与pNFS之间添加pNFS shim layer模块的方法,实现了pNFS支持Hadoop大数据访问的HDFS APIs;通过在pNFS shim layer中添加写缓存和节点级数据布局感知机制优化了系统性能。采用Hadoop基准程序对本文提出的框架进行测试,结果显示写性能提升超过45%,读性能提升超过97%,证明此框架可以有效的支持Hadoop大数据访问。