FEP:一个软硬件集成的闪存数据管理实验平台

闪存以及基于闪存的固态硬盘(SSD)近年来得到了快速发展,从而也带动了闪存相关的算法研究.这些算法不仅包括DBMS层面的算法,也包括SSD内部的控制算法.但是,这些算法的性能验证是目前研究中的一个难点问题,尤其是SSD内部的算法很难在现有的平台上进行实验对比.针对这一问题,提出了一个软硬件集成的闪存数据管理实验平台(flash experimental platform,FEP).该平台不仅可以为DBMS层面各类算法提供一个实验环境,也能够为SSD内部算法的验证提供有效的支持.FEP平台的硬件部分由一个自行设计的闪存存储板构成,软件部分实现了闪存驱动和闪存转换层(FTL)算法,并提供了灵活易用的二次开发接口.以4种闪存缓冲区置换算法为例,在FEP平台上进行了实现和实验验证,结果表明,FEP平台可以有效地支持闪存相关算法的实验和结果数据分析.     

闪存数据库缓冲区置换算法综述

随着闪存技术的发展和闪存容量的不断增大,闪存存储被广泛应用,给闪存数据库管理带来机遇和挑战。因为闪存和磁盘读写方式不同,读写性能也有差别,所以对于闪存缓冲区的管理成为一个亟待解决的问题。为了提高闪存的访问性能,缓冲区置换算法在保证命中率的同时要尽量减少写和擦除操作的次数。对主流的闪存数据库缓冲区置换算法进行分析,比较了几种算法的优点和不足,并给出了未来研究的方向。     

FClock:一种面向SSD的自适应缓冲区管理算法

现有的各种基于闪存的缓冲区管理算法针对闪存读写代价的不对称性进行改进,实际中既存在同一闪存读写代价的不对称性问题,也存在不同闪存不对称性之间的巨大差异性问题,而后者一直没有得到足够的重视.文章提出一种基于闪存硬盘(SSD)的自适应缓冲区管理算法FClock,FClock将数据页组织为两个环形数据结构(CC和DC),分别用于存储缓冲区中的只读数据页和已修改数据页.当需要选择置换页时,FClock使用基于代价的启发式来选择置换页,可在未修改的数据页和已修改的数据页之间进行公平的选择,适用于不同种类的SSD.针对数据库、虚存和文件系统中数据页访问存在高相关性的特点,提出基于"平均命中距离"的访问计数方法来调整数据页的访问频率.基于不同SSD和不同存取模式的实验结果说明,FClock的综合性能优于已有方法.     

基于序列模式的Servlet容器缓存替换

Servlet缓存能够有效地提高Servlet容器的吞吐量,缩短用户请求的响应时间.然而,Servlet缓存的性能受到缓存替换算法的影响.Servlet容器中的Servlet对应着一定的业务功能,挖掘Servlet之间的业务关联来指导缓存替换算法的设计可以提高Servlet缓存的命中率,进而提高Servlet容器的性能.然而,目前常见的LRU(least recently used),LFU(least frequently used),GDSF(greedy dual size frequency)等缓存替换算法均没有考虑上述问题.将Servlet对应的业务关联定义为Servlet容器序列模式,并提出k步可缓存转移概率图的概念加以表示,给出了序列模式发现算法KCTPG_Discovery.最后,基于Servlet容器序列模式设计了缓存替换算法KP-LRU(k-steps prediction least recently used)和KP-GDSF(k-steps prediction least frequently used).实验结果表明,KP-LRU与KP-GDSF算法比对应的LRU算法和GDSF算法具有更高的缓存命中率,有效地提高了Servlet容器的性能.     

基于ARC的闪存数据库缓冲区算法

闪存是一种纯电子设备,具备体积小、数据读取速度快、能耗低、抗震性强等优点,被用来部分替代机械硬盘从而提升存储系统的性能.但是,现有的缓冲区置换算法都是针对机械硬盘的物理特性进行设计和优化,因此有必要针对闪存的物理特性重新设计缓冲区置换算法.提出一种新的面向闪存数据库的缓冲区替换算法CF-ARC.算法设计了一种新的页替换机制,即在替换干净页或者脏页的时候考虑其访问频度的大小,优先将访问频度少的干净页替换出缓冲区,使得热页继续留在缓冲区提高命中率,从而获得更好的性能,通过对实验结果的对比分析发现CF-ARC在多数情况下具有比其它置换算法更高的性能.     

一种基于闪存的缓冲区管理算法

在大多数以磁盘为存储系统的操作系统中,缓冲区管理算法只考虑到了数据访问的命中率。然而,闪存的写操作代价远远大于读操作代价。为了提高闪存性能,本文提出的O CFLRU（Optimal CFLRU）算法对于CFLRU（Clean First LRU）算法做了优化。该算法用一种页 块混合的数据结构来分别管理缓冲区中的干净页面和脏的数据页面聚簇。当缓冲区空间不够时,优先置换干净页面,再置换出脏的数据页聚簇,从而减少了写回的次数和随机写带来的擦除次数,提高了闪存的性能。     

集群VOD系统中磁盘Cache替换算法研究

根据VOD的特点开发了两种基于访问频率的替换算法：LFRU(least frequeney and recently used)和PLFU(period least frequency used)算法，它们都试图将访问频率大的视频数据保留在Cache中。LFRU算法结合了数据的访问频率和访问时间信息，对访问模式的变化具有一定的适应性。PLFU算法用周期法和预测法解决TLFU算法中的Cache“污染”问题。     

一种对LRFU置换策略的自适应改进

数据库缓冲区页面置换算法对磁盘数据库的性能有着重要的影响,页面置换算法主要有基于访问时间的置换策略、基于访问次数的置换策略、两者结合的置换策略等。LRFU算法是一系列结合LRU和LFU置换策略的置换算法,很好地实现了两种置换策略的结合,但却没有给出一种在不同的应用场景中进行动态调整的机制。提出了一种对LRFU算法进行动态调整的方法,模拟测试发现改进的LRFU算法都不同程度地提高了缓冲区命中率。     

一种面向工业边缘计算应用的缓存替换算法

工业应用对数据传输的确定性有严格要求,有必要通过合理的缓存策略保障工业边缘网络的实时服务性能保障.首先面向工业边缘计算应用场景阐述了边缘缓存问题模型.然后分析了工业应用中用户请求的动态性特点,结合工业用户请求的特征属性,给出用户请求内容流行度变化的预测方法.在此基础上提出了基于属性特征流行度预测的缓存替换(combing periodic popularity prediction and size caching strategy,PPPS)算法,根据最近周期窗口内主导属性特征的热度预测值,和尺寸参数一起确定缓存内容价值.实验结果表明:与MPC(most-popular content)、贪婪双尺寸(greedy dual size,GDS)、最近最久未使用(least recently used,LRU)、最近最少访问频次(least frequently used,LFU)、先进先出(first in first out,FIFO)这5种经典算法相比,提出的PPPS算法在缓存命中率和平均延迟2种性能指标下,在不同的用户请求模型、内容大小分布、内容种类参数下均取得最优性能,有效提升了边缘缓存的命中率,提高了缓存利用效率,降低了用户请求内容的延迟.     

基于代价的闪存数据库缓冲区置换算法

提出一种基于闪存硬盘(solid state disk,简称SSD)的自适应缓冲区管理算法CBLRU,其将数据页的置换代价与其驻留内存的影响相结合,为每个数据页附加一个权值,当发生页缺失问题时,选择具有最小权值的数据页进行置换,从而可以在延长修改页驻留缓冲区的同时,避免某些修改页长期占用缓冲区中有效空间问题的发生.由于...     

一种闪存敏感的多级缓存管理方法

基于闪存的固态硬盘(solid state driver,简称SSD)已经广泛应用于各种移动设备、PC机和服务器.与磁盘相比,尽管SSD具有数据存取速度高、抗震、低功耗等优良特性,但SSD自身也存在读写不对称、价格昂贵等不利因素,这使得SSD 短期内不会完全取代磁盘.将SSD和磁盘组合构建混合系统,可以发挥不同的硬件特性,提升系统性能.基于 MLC 型 SSD 和 SLC 型 SSD 之间的特性差异,提出了一种闪存敏感的多级缓存管理策略——FAMC.FAMC将SSD用在内存和磁盘之间作扩展缓存,针对数据库系统、文件管理中数据访问的特点,有选择地将内存牺牲页缓存到不同类型的SSD.FAMC同时考虑写请求模式和负载类型对系统性能的影响,设计实现对SSD友好的数据管理策略.此外,FAMC基于不同的数据置换代价提出了适用于SSD的缓冲区管理算法.基于多级缓存存储系统对FAMC的性能进行了评测,实验结果表明,FAMC可以大幅度降低系统响应时间,减少磁盘I/O.     

On using cache conscious clustering for improving OODBMS performance

The two main techniques of improving I/O performance of Object Oriented Database Management Systems (OODBMS) are clustering and buffer replacement. Clustering is the placement of objects accessed near to each other in time into the same page. Buffer replacement involves the selection of a page to be evicted, when the buffer is full. The page evicted ideally should be the page needed least in the future. These two techniques both influence the likelihood of a requested object being memory resident. We believe an effective way of reducing disk I/O is to take advantage of the synergy that exists between clustering and buffer replacement. Hence, we design a framework, whereby clustering algorithms incorporating buffer replacement cache behaviour can be conveniently employed for enhancing the I/O performance of OODBMS. We call this new type of clustering algorithm, Cache Conscious Clustering (C3). In this paper, we present the C3 framework, and a C3 algorithm that we have developed, namely C3-GP. We have tested C3-GP against three well known clustering algorithms. The results show that C3-GP out performs them by up to 40% when using popular buffer replacement algorithms such as LRU and CLOCK. C3-GP offers the same performance as the best existing clustering algorithm when the buffer size compared to the database size is very small.     

Buffer management in a real-time shared disks cluster

A great deal of research indicates that the shared disks (SD) cluster is suitable to high performance transaction processing. However, the aggregation of SD cluster with real-time processing has not been investigated. By adopting cluster technology, the real-time services will be highly available and can exploit internode parallelism. In this paper, we consider buffer management issues in a real-time SD cluster. We first propose a real-time buffer coherency algorithm that exploits inherent characteristics of real-time applications and SD cluster. Then we extend traditional buffer replacement algorithms to the real-time SD cluster. They emphasize specific attributes of buffer pages to capitalize locality of references, transaction deadline, and affinity-based routing. We evaluate the performance of the proposed algorithms under a wide variety of transaction workloads and system configurations.     

Time-shift replacement algorithm for main memory performance optimization

Page replacement algorithms of main memory in modern operating systems are crucial in system performance. When memory is full, a page replacement algorithm exploits temporal locality and frequency of page references to evict the page that is least likely to be accessed in the near future. Subsequently, loading the majority of data directly from memory improves performance by reducing I/O waits of accessing slow storage. Research of replacement algorithms that maximizes hit ratio while incurring as less overhead as possible has been constantly studied. In this paper, we propose a time-shift least recently used (TSLRU) algorithm that converts frequency information of page references into temporal locality. Frequent accesses of a page are thus recognized and accumulated in terms of time. Moreover, pages being loaded into memory for the first time are not necessarily the most recently used pages. As a result, one-pass pages are evicted sooner in our algorithm than in traditional LRU algorithm. Our performance evaluations show that the TSLRU outperforms conventional page replacement algorithms on both artificial and real application traces. For example, hit ratio of TSLRU advances ARC by \(4.17\%\) and LRU by \(5.91\%\) on normal distributed workloads. Moreover, TSLRU outperforms ARC by over \(2\%\) on half of the application traces tested.     

ARIES算法在PostgreSQL中的实现

通过现有的PostgreSQL的恢复算法和ARIES算法的比较,说明采用ARIES算法来改进PostgreSQL的必要性,并在分析现有的PostgreSQL与恢复相关的几个实现策略上,介绍为采用ARIES算法而设计的主要数据结构以及如何改进事务恢复管理器以便支持ARIES算法的事务基本操作和恢复管理的实现流程。这样比较显著地提高了PostgreSQL的事务处理能力。     

Second-level buffer cache management

Buffer caches are commonly used in servers to reduce the number of slow disk accesses or network messages. These buffer caches form a multilevel buffer cache hierarchy. In such a hierarchy, second-level buffer caches have different access patterns from first-level buffer caches because accesses to a second-level are actually misses from a first-level. Therefore, commonly used cache management algorithms such as the least recently used (LRU) replacement algorithm that work well for single-level buffer caches may not work well for second-level. We investigate multiple approaches to effectively manage second-level buffer caches. In particular, we report our research results in 1) second-level buffer cache access pattern characterization, 2) a new local algorithm called multi-queue (MQ) that performs better than nine tested alternative algorithms for second-level buffer caches, 3) a set of global algorithms that manage a multilevel buffer cache hierarchy globally and significantly improve second-level buffer cache hit ratios over corresponding local algorithms, and 4) implementation and evaluation of these algorithms in a real storage system connected with commercial database servers (Microsoft SQL server and Oracle) running industrial-strength online transaction processing benchmarks.     

FUZZY REPLACEMENT ALGORITHM FOR CACHE MEMORY

This paper reports a replacement procedure for cache memories. The procedure is essentially a fuzzy algorithm that makes use of 18 rules to select the cache block to be replaced. These rules are primarily a function of three parameters: age and the frequency of usage of a cache block, and the global hit ratio of the cache system. Computationally, the proposed algorithm calculates a replacement index for each cache block and the block with highest replacement index is selected as a victim. The performance of this fuzzy procedure is compared with traditional replacement algorithms such as least recently used (LRU) and first in First out (FIFO). Our simulation experiments indicate that the proposed algorithm is a strong contender to the traditional counterparts. The unique feature of the proposed algorithm is its flexibility; that is, one can always improve its performance further by fine-tuning the rules.