面向SSD/HDD混合存储的动态缓存调度算法DRC

云计算与大数据的发展,对存储系统的性能提出了越来越高的要求。这些系统中的任务具有高并发度特征,使得存储系统的数据访问呈现随机化。SSD具有优异的随机读写性能,但其写入次数受到限制,成本高昂,因此,基于SSD-HDD的混合系统成为存储技术发展的主要方向。面向SSD-HDD混合存储提出了一种基于动态替换代价的缓存调度算法(DRC),以请求中的热点数据以及替换数据的代价作为缓存替换依据,不仅有效地提高了缓存命中率,而且,通过减少磁盘随机写操作提升了系统的整体性能。实验结果表明,在高并发读写的场景下,DRC算法相对于LRU或FIFO算法缓存命中率提升可达11.6%,IO速度最多提升16.7%,在各种缓存大小条件下均取得了显著的性能提升。     

面向SSD寿命优化的访问序列折叠缓存替换算法*

SSD(solid state drive)的写入寿命比较有限,因此除命中率外,SSD缓存设备的写入量成为评价缓存替换算法的另一个关键指标。如何使算法提高写入数据转化为缓存命中的效率,从而延长SSD的使用寿命,具有重要的研究意义。目前,已有缓存替换算法的设计一般基于时间局部性,即刚被访问的数据短期内被访问的概率较高,因此需要频繁的数据更新和较高写入量来保证较高命中率;或是通过不低的开销屏蔽相对最差的部分数据来减少一定的写入量,还缺少用低开销获得数据长期热度规律,有效提高缓存数据质量的算法。提出了访问序列折叠的缓存替换算法,用比较低的开销定位拥有长期稳定热度的数据写入缓存,明显提高了SSD缓存数据质量,在保证命中率的同时减少了SSD的写入量。实验表明,访问序列折叠算法相比LRU(least recently used)算法可在命中率损失低于10%的情况下减少90%的写入量,与SieveStore、L2ARC(level2 adjustable replacement cache)等写入优化缓存算法相比,命中率相当时可将写入量减少50%以上,有效达到了通过缓存高质量数据,减少SSD的写入量,延长其使用寿命的目的。     

基于顺序检测的双队列缓存替换算法

缓存技术是提高存储性能最有效的技术之一,在存储系统中得到了广泛应用.由于缓存容量有限,替换算法在缓存策略中占据了重要地位.当前,缓存替换算法的研究工作主要集中在如何提高缓存系统命中率,忽略了通过降低缓存失效开销来提高缓存系统性能方面的研究.针对这一问题,本文提出了一种基于顺序检测的双队列缓存替换算法:本算法优先淘汰缓存中的顺序页面,保留随机页面,从而大大减少后续请求对磁盘进行随机访问的次数,能够显著降低缓存系统的失效开销.同时,本算法使用两个队列分别维护新加入页面和待淘汰页面,遵循时间局部性原理,保证了缓存命中率.实验结果表明,本算法在多种缓存大小及工作负载下,可以达到比LRU和ARC算法更优的性能.     

对象存储中基于高斯分布的分层缓存淘汰算法

在分布式存储系统中,引入分层缓存技术是优化系统读写的重要方法。目前分层缓存技术多使用LRU及其改进算法管理缓存空间,该方式虽然一定程度上改善了缓存性能,但有限的缓存命中率也使其成为性能瓶颈。针对此问题,本文研究聚焦于分布式对象存储在云计算场景下的应用特点,设计了一种基于高斯分布的淘汰算法。算法基于高斯分布的形态特征设计淘汰规则,规避了LRU及其改进算法采用频率估计概率的误差影响。通过仿真实验结果表明,在用户访问符合高斯分布时,本文提出的基于高斯分布的分层缓存淘汰算法能有效提高缓存命中率。     

基于高斯混合模型的Web代理服务器缓存替换策略

Web代理服务器缓存能够在一定程度上解决用户访问延迟和网络拥塞问题,Web代理缓存的缓存替换策略直接影响缓存的命中率,从而影响网络请求响应的效果;为此,使用一种通过固定大小的循环滑动窗口提取Web日志数据的多项特征,并使用高斯混合模型对Web日志数据进行聚类分析,预测在窗口时间内可能再次访问到Web对象,结合最近最少使用(LRU)算法,提出一种新的基于高斯混合模型的Web代理服务器缓存替换策略;实验结果表明,与传统的缓存替换策略LRU、LFU、FIFO、GDSF相比,该策略有效提高了Web代理缓存的请求命中率和字节命中率。     

多维数据的Z-Ordering存储映射算法及其缓存调度优化

多维数据以线性形式在存储系统中进行访问操作,二维及以上维度空间中的相邻节点被不同的映射算法映射到一维空间的不相邻位置。高维空间中进行相邻节点访问时,其一维存储映射位置有着不同的访问距离和访问延迟。提出了基于空间填充曲线Z-Ordering的存储映射方法及其访问距离的度量指标,并和常规优先算法进行了对比,发现能更好地将高维相邻的数据节点簇集到一维存储位置,加强了局部性。调整缓存空间中用于预取的空间大小,可以利用增强的局部性,提高了缓存命中率。实验结果表明,改善了多维数据的访问速度,优化了系统性能。     

普适环境中面向推理的上下文缓存置换算法

上下文缓存是减少上下文信息访问开销、降低信息传输数量、缓解连接中断引起的程序不可用性的有效途径.面向推理的上下文缓存置换算法CORA的目标是使上下文缓存达到较高命中率,有效节省普适计算中传输上下文的开销.CORA采用状态空间对低级上下文到高级上下文的推理进行建模,对各种上下文推理方法具有普遍适用性.CORA算法分为两个部分:1)在缓存端,该算法计算低级上下文的访问概率和预计失效时间,获得数据的缓存价值,作为上下文缓存置换的依据,以提高缓存的命中率;2)在传感器端设置相应的可变化范围,当传感器读数超出该范围时,主动更新缓存,以保证缓存数据的一致性.模拟实验将CORA和经典的缓存置换算法LRU进行对比,分别通过改变缓存容量、对上下文访问概率的不均匀程度和上下文更新访问比来考察两种算法的命中率,结果显示,当缓存容量相对上下文总数较小、访问概率分布较不均匀、更新访问比较高的情况下,CORA的命中率大大高于LRU.由此证明,CORA更适用于较为动态的普适计算环境.     

Web合作缓存置换算法的改进与实现

在LRU算法的基础上,提出一种改进的Web合作缓存置换算法。该算法针对不同大小的文档采取不同的存储策略,如增加小文档在缓存组中的存储数量,以提高其本地缓存的命中率,减少大文档在缓存组中存储的数量,以节约整个缓存组的空间。仿真实验结果表明该算法能够获得较好的性能。     

Web缓存层次模型与代价分析

Web缓存分层结构在避免单点失效、提高缓存性能方面具有重要作用.论文研究了Web缓存层次模型,提出请求分发的三种模式,并利用代价函数分析探讨了缓存模型性能.根据Web访问共同特征,实验采用数学建模方法生成模拟日志,模拟不同层采用不同替换算法(LRU、LFU、GDS)时的缓存性能.结果表明,模拟日志的高频区、低频区流行度访问特征分别服从齐普夫第一定律、第二定律,具有真实日志的特性,能够模拟用户请求评价Web缓存层次模型性能;当低层代理缓存采用LFU或LRU替换算法,高层代理缓存采用GDS替换算法时,两层缓存模型在命中率、字节命中率方面有较好的性能表现.     

一种数据块关系指导的缓存替换方法

提出了一种数据块关系指导的缓存替换方法BDP(block correlations directed replacement policy).数据块关系所表示的空间局部性可以用来指导存储系统的缓存替换策略.BDP利用历史访问信息和实时访问信息,预测数据块未来的空间局部性特征,并设计数据块驻留时间的控制策略,减少现有替换算法对空间局部性预测失败带来的失效惩罚.同时给出了BDP的有效缓存模型.模拟结果显示,对大多数实际系统工作负载,BDP的失效率相对LRU算法下降11%～38%,优于现有的考虑空间局部性的替换算法.     

HAT: an efficient buffer management method for flash-based hybrid storage systems

Flash solid-state drives (SSDs) provide much faster access to data compared with traditional hard disk drives (HDDs). The current price and performance of SSD suggest it can be adopted as a data buffer between main memory and HDD, and buffer management policy in such hybrid systems has attracted more and more interest from research community recently. In this paper, we propose a novel approach to manage the buffer in flash-based hybrid storage systems, named hotness aware hit (HAT). HAT exploits a page reference queue to record the access history as well as the status of accessed pages, i.e., hot, warm, and cold. Additionally, the page reference queue is further split into hot and warm regions which correspond to the memory and flash in general. The HAT approach updates the page status and deals with the page migration in the memory hierarchy according to the current page status and hit position in the page reference queue. Compared with the existing hybrid storage approaches, the proposed HAT can manage the memory and flash cache layers more effectively. Our empirical evaluation on benchmark traces demonstrates the superiority of the proposed strategy against the state-of-the-art competitors.     

视频访问负载的分级存储及迁移算法研究

为了使分布式分级混合存储系统高效、快速响应的工作,达到优化系统性能和减少系统资源消耗的目标,通过研究负载的模式,同时考虑数据访问局部性和系统响应时间,提出了相应于负载识别、以及基于频率策略和带宽策略的分级存储迁移算法,提出了一种目标函数为(带宽节省率/命中率)的新评价标准。频率策略是根据访问的周期频率特性来进行数据分级存储迁移,带宽策略是根据在访问中考虑迁移带宽消耗特性来进行数据分级存储迁移。结合实例,模拟仿真实验结果表明,两种策略都能有效地到达目标。频率策略带来的访问次数及命中率较高,而带宽策略可以减少分级存储并发瓶颈数量。     

一种基于四叉树的空间数据缓存策略

提出了以四叉树作为缓存数据结构,结合广泛应用的LRU和LFU算法,给出了一种高效的缓存策略—基于四叉树的空间数据缓存策略,并详细描述了缓存框架和缓存策略。提出的缓存策略充分考虑了空间数据访问所具有的时间局部性和空间局部性,兼有LRU和LFU算法的优点。最后设计了空间数据请求模型,通过实验对算法的有效性进行了验证。     

基于SSD的机群文件系统元数据存储系统

随着数据量的增加和元数据操作性能需求的提高,传统基于磁盘(HDD)存储架构的机群文件系统元数据存储系统由于HDD成为性能瓶颈而无法满足需求.将SSD应用到元数据存储中,设计实现了一个基于SSD+HDD的异构元数据存储系统Hybrid MDSL.针对SSD的I/O特性设计了基于追加写的元数据存储组织,并通过基于访问热度的数据迁移机制提高SSD空间利用率.测试结果表明,Hybrid MDSL明显提高了元数据I/O的性能.     

Miss-aware LLC buffer management strategy based on heterogeneous multi-core

When multiple processor (CPU) cores and a GPU integrated together on the same chip share the last-level cache (LLC), the competition for LLC is more serious. CPU and GPU have different memory access characteristics, so that they have differences in the sensitivity of LLC capacity. For many CPU applications, a reduced share of the LLC could lead to significant performance degradation. On the contrary, GPU applications have high number of concurrent threads and they can tolerate access latency. Taking into account the GPU program memory latency tolerance characteristics, we propose an LLC buffer management strategy (buffer-for-GPU, BFG) for heterogeneous multi-core. A buffer is added on the side of LLC to filtrate streaming requests of GPU. Cache-insensitive GPU messages directly access to buffer instead of accessing to LLC, thereby filtering the GPU request and freeing up the LLC space for the CPU application. Then, for the different characteristics of CPU and GPU applications, an improved LRU replacement taking into account the recent access time and access frequency of the cache block is adopted. The cache misses-aware algorithm dynamically selects the improved LRU or LRU algorithm to fit the current operating state by comparing the miss rate of cache in buffer so that the performance of the system will be improved significantly.

     

面向本地分布式存储系统的动态副本策略

针对本地分布式存储系统的拓扑结构与存储组成单元的特性,提出了一种基于频度的动态副本算法FBDR。FBDR分别采用基于密度的单次频度分析和基于区间长度的两次频度聚合的方法对文件访问流进行分析,作为确定热点文件的依据,具有较高的命中率。在副本创建位置选择上,综合考虑了存储单元的可用空间、负载、IO性能等因素,使热点文件获得更高的IO速率,同时兼顾了存储单元之间的负载与资源利用的平衡。     

Heterogeneous-aware cache partitioning: Improving the fairness of shared storage cache

In this paper, we investigate the problem of fair storage cache allocation among multiple competing applications with diversified access rates. Commonly used cache replacement policies like LRU and most LRU variants are inherently unfair in cache allocation for heterogeneous applications. They implicitly give more cache to the applications that has high access rate and less cache to the applications of slow access rate. However, applications of fast access rate do not always gain higher performance from the additional cache blocks. In contrast, the slow application suffer poor performance with a reduced cache size. It is beneficial in terms of both performance and fairness to allocate cache blocks by their utility.In this paper, we propose a partition-based cache management algorithm for a shared cache. The goal of our algorithm is to find an allocation such that all heterogeneous applications can achieve a specified fairness degree as least performance degradation as possible. To achieve this goal, we present an adaptive partition framework, which partitions the shared cache among competing applications and dynamically adjusts the partition size based on predicted utility on both fairness and performance. We implement our algorithm in a storage simulator and evaluate the fairness and performance with various workloads. Experimental results show that, compared with LRU, our algorithm achieves large improvement in fairness and slightly in performance.     

Prober: exploiting sequential characteristics in buffer for improving SSDs write performance

Solid state disks (SSDs) are becoming one of the mainstream storage devices due to their salient features, such as high read performance and low power consumption. In order to obtain high write performance and extend flash lifespan, SSDs leverage an internal DRAM to buffer frequently rewritten data to reduce the number of program operations upon the flash. However, existing buffer management algorithms demonstrate their blank in leveraging data access features to predict data attributes. In various real-world workloads, most of large sequential write requests are rarely rewritten in near future. Once these write requests occur, many hot data will be evicted from DRAM into flash memory, thus jeopardizing the overall system performance. In order to address this problem, we propose a novel large write data identification scheme, called Prober. This scheme probes large sequential write sequences among the write streams at early stage to prevent them from residing in the buffer. In the meantime, to further release space and reduce waiting time for handling the incoming requests, we temporarily buffer the large data into DRAM when the buffer has free space, and leverage an actively write-back scheme for large sequential write data when the flash array turns into idle state. Experimental results demonstrate that our schemes improve hit ratio of write requests by up to 10%, decrease the average response time by up to 42% and reduce the number of erase operations by up to 11%, compared with the state-of-the-art buffer replacement algorithms.     

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.     

混合架构下多请求模式的缓存替换模型研究

针对多类型多访问模式应用的需求,在GDSF算法的基础上,引入平均访问间隔和最近访问间隔两个特性以增强算法的适应性;建立缓存结构模型,通过双关键字索引机制,快速索引缓存对象,降低系统开销;对超过一定大小的文件采取后缀预取策略以增加缓存中数据对象的个数.在课题应用背景下,与传统算法的对比实验表明,该方法能够减少缓存的平均请求等待时间,提高对象命中率和字节命中率,增强了缓存替换算法对多类型多请求模式应用的适应性.