多用户存储中自适应动态预取策略

通过分析多用户数据请求规律以及实时分解随机请求序列来获取顺序请求序列。基于对多用户顺序请求进行命令预分解和命中率统计,实现读预取长度自我学习。分析多用户预取率及系统负载与预取失效代价之间的关系,对常规自适应Cache策略进行优化,选择合适预取阈值等参数。与常规自适应预取策略相比,动态调整Cache策略的预取命中率提高了30%。有效解决了多用户访问共享存储系统的预取失效率高问题。     

PostgreSQL数据库预取算法研究

减少磁盘的存取时间是提高数据库性能的关键.本文讨论了PostgreSQL数据库顺序存取的特性,提出了一种PostgreSQL中顺序预取数据块的算法,预取的数据块数目可根据当前存取块之前的顺序存取情况作自适应调整.实验结果表明,该算法能有效地提高磁盘块的平均存取速度.     

关于条式磁盘阵列中长短数据混合存取的研究

本文对条式磁盘阵列中的长短数据混合存取的若干问题进行了分析和讨论，主要讨论了短数据存取后，因各驱动器磁头的磁道位置不同引起后面的长数据存取时的寻道时间的增加，以及长短数据混合存取时的平均存取时间等方面的内容。     

基于简单常见模式编码（S-FPC）的压缩Cache层次设计

本文基于简单常见模式压缩编码设计了一种新颖的片内压缩Cache层次结构。在该结构中,L1数据Cache和L2Cache都以压缩格式保存数据,但具有不同的布局。其中,L1数据Cache的布局能触发部分Cache行预取,同时又能避免普通预取技术可能导致的Cache污染增加以及带宽浪费的现象,而且没有预取缓冲开销。实验结果表明,与传统Cache结构相比,本文的设计方案可以显著增加L1数据Cache和L2Cache的有效容量,并且不会增加L1数据Cache的访存延迟,对L1数据Cache平均能增加33％的有效容量,减少L1数据Cachhe失效率达21％,程序执行速度提高了13％。     

排队论在计算机存储系统性能中的应用和分析

I/O响应时间是衡量存储系统性能的重要指标。本文基于使用光纤通道磁盘阵列构建的存储区域网环境,利用排队论分析了不同预取策略对磁盘阵列I/O响应时间的影响,提出改进存储系统性能的方法。     

嵌入式移动数据库客户端列Cache方案研究

在嵌入式环境中，有效利用客户机端Cache的空间，可以大大降低系统功耗和提高系统的性能。从体系结构级的角度，介绍了一种适应数据广播环境的移动数据库客户机端Cache管理方案——列Cache，并探讨了一种改进的替换和预取策略——带锁的循环淘汰PIX算法，分析了其可行性，试验证明了这种方案的有效性。     

管道泄漏监测的传感器网络中数据存取算法实现

针对管道泄漏和管网突发性的爆管,将自适应数据存取设计应用于管道流量泄漏监测中,数据存取是指生产者将感知数据按照策略存放在特定的位置上,消费者将查询请求按照对应策略路由到数据存放位置获得感兴趣的数据.首先依据生产者和消费者关系建模"一对一"、"多对一"、"多对多"模型来对存取代价进行分析.其次确定数据存放位置的自适应全局最优贪婪算法ODS和局部最优近似算法NDS以及最优数据传输模式.最后ODS和NDS通过自适应调整来减少数据存取能量消耗.实验表明NDS不仅节省能耗,而且在70%的情况下达到与ODS相同的效果.     

pT-树:高速缓存优化的主存数据库索引结构

随着主存速度和现代处理器速度之间的差距逐渐扩大,系统对主存的存取访问成为新的瓶颈,Cache行为对主存数据库系统更加重要.索引技术是主存数据库系统设计的关键部分.在CST-树的基础上应用预取技术提高查找操作的性能,提出了一种Cache优化的索引结构预取T-树(pT-tree).pT-树使用预取技术有效地创建比正常数据传...     

文件Cache自适应策略研究

Linux系统在被不同大小的数据块访问时,系统读写性能有差异。在少数特定访问数据块大小的应用中,Linux系统读写性能较差。文件Cache算法的性能是导致该问题的原因之一。在分析访问数据块大小对文件Cache算法性能的影响的基础上,提出了一种文件Cache自适应策略。该策略考虑了预取算法对于页面置换算法的影响,增强了页面置换算法对访问数据块大小变化的适应性,达到了提高Linux系统读写性能的目标。Linux系统读写性能测试实验表明,该策略可以使Linux系统在被不同大小的数据块访问时保持稳定且更优的读写性能。     

关系模式下的XML数据存取技术研究

XML数据具有自描述性和半结构化特征，所以它能够从自身得到能够描述自己的类似于数据库的数据模式，并且对XML数据进行数据库存取。本文对利用关系模式来映射XML模式从而在关系数据库中存取XML数据的技术，进行了研究和初步应用。     

面向多应用环境RAID系统的智能预取和缓存调度

本文分析了RAID系统的多应用环境数据请求的存储模式的特点，提出了能根据应用环境的不同而自动改变预取策略的智能预取算法以及缓存调度算法。实践证明，本算法使得RAID系统的预取和缓存调度摆脱了盲目性，保证了预取策略和缓存调度的最优性。     

Multiple prefetch adaptive disk caching

A new disk caching algorithm is presented that uses an adaptive prefetching scheme to reduce the average service time for disk references. Unlike schemes which simply prefetch the next sector or group of sectors, this method maintains information about the order of past disk accesses which is used to accurately predict future access sequences. The range of parameters of this scheme is explored, and its performance is evaluated through trace-driven simulation, using traces obtained from three different UNIX minicomputers. Unlike disk trace data previously described in the literature, the traces used include time stamps for each reference. With this timing information-essential for evaluating any prefetching scheme-it is shown that a cache with the adaptive prefetching mechanism can reduce the average time to service a disk request by a factor of up to three, relative to an identical disk cache without prefetching     

代理缓存关键技术的研究

基于因特网的代理缓存技术是解决Web访问速度慢、服务器负载重和网络阻塞等问题的一种主要和有效的技术。为了能设计出有效、可扩展、健壮、自适应和稳定的代理缓存方案，本文主要对代理缓存的一致性策略、替换策略、体系结构、缓存内容选择和预取等关键技术问题进行研究，并给出了相关技术的解决方案。     

一种步长自适应二级cache预取机制

随着集成电路制造工艺的快速发展,片上实现大容量的cache成为可能,这从很大程度上降低了cache的失效率,与此同时,大容量的cache发生失效时的开销也更加显著。通过分析cache失效行为,设计了一种新的二级cache步长自适应预取机制,该机制充分利用了二级cache对指令地址不可见的特点,使用失效地址作为索引检查预取表。通过分析测试结果,选择了合适的结构参数,有效提高了cache性能。     

深度学习在多核缓存预取中的应用研究综述

当前人工智能技术应用于系统结构领域的研究前景广阔,特别是将深度学习应用于多核架构的数据预取研究已经成为国内外的研究热点。针对基于深度学习的缓存预取任务进行了研究,形式化地定义了深度学习缓存预取模型。在介绍当前常见的多核缓存架构和预取技术的基础上,全面分析了现有基于深度学习的典型缓存预取器的设计思路。深度学习神经网络在多核缓存预取领域的应用主要采用了深度神经网络、循环神经网络、长短期记忆网络和注意力机制等机器学习方法,综合对比分析现有基于深度学习的数据预取神经网络模型后发现,基于深度学习的多核缓存预取技术在计算成本、模型优化和实用性等方面还存在着局限性,未来在自适应预取模型以及神经网络预取模型的实用性方面还有很大的研究探索空间和发展前景。     

The Japan Cache Project: an experiment on domain cache

This paper proposes the concept of a domain cache, which is dedicated to handling access to a particular domain name, and reports experiments in the Japan Cache Project which operates a public cache server for access to JP (Japan) domain web servers. The goal of this project is to spread information about Japan to North America. This project included some experiments in prefetching information to makes use of less crowded access times, maintaining the coherence between the cache server and primary servers by primary server refresh, and operating a cache replication server, which keeps the primary server information in the cache and functions not as a proxy server but as an HTTP server.The high cache hit ratio obtained suggests that domain caching is an effective cache server operation method. However, we could not perform effective prefetching because of the difficulty in predicting user accesses. Coherence maintaining and cache replication server mechanisms were created. We appeal for cooperation from servers in Japan.     

移动数据库中改进的CMIP数据预取策略研究

数据预取是移动数据库缓存技术中的关键,CMIP预取策略通过客户端历史访问记录关联规则的挖掘得到预取数据,使系统性能得到了提高。但由于没考虑到数据的更新率及数据大小,将会经常发生缓存失效。在此算法的基础上增加对数据更新率及大小的判断并对所选数据排序,然后进行预取数据的选择。通过改进降低了缓存的失效率、减少了数据访问的时间及电能的消耗。     

Automatic Skeleton-Driven Memory Affinity for Transactional Worklist Applications

Memory affinity has become a key element to achieve scalable performance on multi-core platforms. Mechanisms such as thread scheduling, page allocation and cache prefetching are commonly employed to enhance memory affinity which keeps data close to the cores that access it. In particular, software transactional memory (STM) applications exhibit irregular memory access behavior that makes harder to determine which and when data will be needed by each core. Additionally, existing STM runtime systems are decoupled from issues such as thread and memory management. In this paper, we thus propose a skeleton-driven mechanism to improve memory affinity on STM applications that fit the worklist pattern employing a two-level approach. First, it addresses memory affinity in the DRAM level by automatic selecting page allocation policies. Then it employs data prefetching helper threads to improve affinity in the cache level. It relies on a skeleton framework to exploit the application pattern in order to provide automatic memory page allocation and cache prefetching. Our experimental results on the STAMP benchmark suite show that our proposed mechanism can achieve performance improvements of up to 46 %, with an average of 11 %, over a baseline version on two NUMA multi-core machines.     

Combining data prefetching with non-blocking loads to alleviate cache pollution effects

Data prefetching is a useful approach for reduction of memory access stalls in many scientific applications. However, it suffers from cache pollution severly in some applications. In this paper, we study the effectiveness of combining data prefetching with non-blocking loads on cache pollution and explain why it shows good result in our simulation.     

Prefetch-aware fingerprint cache management for data deduplication systems

Data deduplication has been widely utilized in large-scale storage systems, particularly backup systems. Data deduplication systems typically divide data streams into chunks and identify redundant chunks by comparing chunk fingerprints. Maintaining all fingerprints in memory is not cost-effective because fingerprint indexes are typically very large. Many data deduplication systems maintain a fingerprint cache in memory and exploit fingerprint prefetching to accelerate the deduplication process. Although fingerprint prefetching can improve the performance of data deduplication systems by leveraging the locality of workloads, inaccurately prefetched fingerprints may pollute the cache by evicting useful fingerprints. We observed that most of the prefetched fingerprints in a wide variety of applications are never used or used only once, which severely limits the performance of data deduplication systems. We introduce a prefetch-aware fingerprint cache management scheme for data deduplication systems (PreCache) to alleviate prefetch-related cache pollution. We propose three prefetch-aware fingerprint cache replacement policies (PreCache-UNU, PreCache-UOO, and PreCache-MIX) to handle different types of cache pollution. Additionally, we propose an adaptive policy selector to select suitable policies for prefetch requests. We implement PreCache on two representative data deduplication systems (Block Locality Caching and SiLo) and evaluate its performance utilizing three real-world workloads (Kernel, MacOS, and Homes). The experimental results reveal that PreCache improves deduplication throughput by up to 32.22% based on a reduction of on-disk fingerprint index lookups and improvement of the deduplication ratio by mitigating prefetch-related fingerprint cache pollution.