视频服务器存储子系统的I/O优化

视频服务器存储子系统的I／O性能决定了视频服务器的总体性能。本文提出的一种新的实时磁盘调度算法（LLF－Window)能有效地服务各种编码格式的视频流，并对传统的SCSI总线不均衡调度机制进行了改造，实验结果表明，新的磁盘调度算法和改造后的SCSI总线调度机制有效地改进了视频服务器存储子系统的I／O性能，保证了视频流的连续播放。     

Linux下一种磁盘节能的预取算法①

数据预取常用来提升系统的性能与吞吐量,对磁盘的能耗考虑甚少。针对此问题,在传统算法之上通过延迟磁盘的异步预取,合并磁盘I／O操作,减少磁盘的能耗状态切换,延长连续休眠时间来达到节能的目的。也通过基于真实运行状态的模拟,对预取算法进行了评估和验证,得出改进后的预取在不影响性能的前提下比标准预取节省17％的能量。     

VBR视频流磁盘I/O的平滑调度算法

文章将带宽平滑技术引入磁盘 I/ O调度 ,基于现已取得的研究成果 ,考虑磁盘 I/ O存取特性 ,提出一个针对VBR视频检索的整数块平滑调度算法 .算法所得的结果被证明是优化的 ,实验结果显示该算法在磁盘空间和磁盘 I/ O的带宽利用方面有很好的性能     

Linux下一种磁盘节能的预取算法①

数据预取常用来提升系统的性能与吞吐量,对磁盘的能耗考虑甚少。针对此问题,在传统算法之上通过延迟磁盘的异步预取,合并磁盘I/O操作,减少磁盘的能耗状态切换,延长连续休眠时间来达到节能的目的。也通过基于真实运行状态的模拟,对预取算法进行了评估和验证,得出改进后的预取在不影响性能的前提下比标准预取节省17%的能量。     

RAID的并行I／O调度算法分析

由于越来越多的应用受限于I／O，存储系统正起着越来越重要的作用，磁盘阵列RAID是一种提供高性能I／O的最常见存储设备，本文分析了RAID并行I／O调度算法的I／O执行时间和磁盘利用率，为合理配置高性能阵列提供了依据。     

Ro-RAT系统随机调度算法

I/O调度对存储系统的性能、响应速度有很大的影响。本文介绍了一种用于地质勘探领域数据存储的Ro-RAT(ReadOnly RAID-tape-library)系统。该系统采用磁盘作为磁带库的缓存系统,以提高整个系统的读取速度。我们设计了一种适合该系统的磁带I/O调度算法SCAN-M。该算法与应用于磁盘I/O调度的电梯算法类似,但针对磁带的特点进行了调度的排序和合并。从仿真结果看,SCAN-M算法对系统的I/O性能有较大的改进。     

一种动态并行I／O调度新策略的研究

并行I／O已经成为提高分布式计算系统性能的一种重要方式，论文在研究集群计算系统并行I／O数据调度策略的基础上，提出了一种应用于分布式计算系统中的二次调度自主维护负载平衡的动态I／O调度算法（DIO_TSMB），实验结果表明了算法的有效性，最后分析了并行I／O数据调度的发展趋势。     

DiskSeen预取算法的分析及优化研究

计算机存储层次结构是一种典型的金字塔形结构,以平衡计算机对存储系统的两方面需求,即高速处理数据和大的存储容量。然而随着信息技术的飞速发展,计算机处理器和磁盘之间的速度鸿沟持续扩大,因而磁盘访问便成为一个 影响 计算机系统性能的瓶颈问题。近几十年来,如何减小磁盘访问延迟对整个计算机系统性能的影响,一直是存储领域的热点研究问题。预取技术,通过提前预测I/O请求并将数据读入缓存中,以对上层应用程序隐藏I/O延迟,是缓解这一瓶颈问题的重要技术手段。DiskSeen是一种块级预取算法,通过分析磁盘块的位置和访问时间的联系来提高磁盘访问的顺序性和总体的预取性能。针对DiskSeen算法,文中主要做了以下几方面工作:首先,分析DiskSeen算法的不足之处,据此提出动态控制预取粒度和二次匹配激活历史预取方法,以优化效率;然后,实现了DiskSeen算法及改进后的算法;最后,在模拟仿真实验环境下对算法进行了性能对比测试。实验结果显示,DiskSeen算法能够明显提高缓存命中率并减少平均响应时间,而优化后的DiskSeen算法则可以进一步提升上述两方面的系统性能。     

Linux I/O调度算法分析与研究

研究表明,好的磁盘调度算法可以明显优化磁盘的I/O性能,Linux提供四种调度算法可供选择。基于Linux 2.6.32内核源码,研究Noop、Deadline、Anticipatory、CFQ四种磁盘调度算法的基本原理和特点,分析每种算法的优缺点,这对系统管理员针对不同类型的应用场景来调整优化I/O调度算法有着重要意义。     

视频服务器中多网络I/O的调度与接纳控制

在视频服务器中,由于网络I/O带宽比磁盘I/O带宽相对丰富而未得到研究者的重视,但当服务达到一定的规模、网络输出成为瓶颈时,在多网络I/O情况下,网络输出的量化计算则不可避免.着重探讨了视频服务器在多网络I/O并行输出的情况下,如何进行最大输出调度的问题,给出了3个调度算法及相应的接纳计算公式,特别是一个带缓冲的优化调度模型.实验结果显示,调度模型及规划性能是优越而有效的.该项工作对视频服务器的调度设计、资源的有效配置具有指导作用.     

Pre-execution data prefetching with I/O scheduling

Parallel applications suffer from I/O latency. Pre-execution I/O prefetching is effective in hiding I/O latency, in which a pre-execution prefetching thread is created and dedicated to fetch the data for the main thread in advance. However, existing pre-execution prefetching works do not pay attention to the relationship between the main thread and the pre-execution prefetching thread. They just simply pre-execute the I/O accesses using the prefetching thread as soon as possible failing to carefully coordinate them with the operations of the main thread. This drawback induces a series of adverse effects on pre-execution prefetching such as diminishing the degree of the parallelism between computation and I/O, delaying the I/O access of main threads, and aggravating the I/O resource competition in the whole system. In this paper, we propose a new method to overcome this drawback by scheduling the I/O operations among the main threads and the pre-execution prefetching threads. The results of extensive experiments on four popular benchmarks in parallel I/O performance area demonstrate the benefits of the proposed approach.     

Performance analysis of advanced I/O architectures for PC-based video servers

In the personal computing and workstation environments, more and more I/O adapters are becoming complete functional subsystems that are intelligent enough to handle I/O operations on their own without much intervention from the host processor. The IBM subsystem control block (SCB) architecture has been defined to enhance the potential of these intelligent adapters by defining services and conventions that deliver command information and data to and from the adapters. In recent years, a new storage architecture, the redundant array of independent disks (RAID), has been quickly gaining acceptance in the world of computing. In this paper, we discuss and present a performance analysis of the SCB architecture and disk array technology in typical video server environments. In particular, we would like to see whether a disk array can outperform a group of disks (of the same type, the same data capacity, and same cost) operating independently (not in parallel as in a disk array) in a video server environment where most disk I/O operations are large sequential reads.     

核外计算中的几种I/O优化方法

大数据量应用问题引入核外计算模式,由于访问磁盘数据的速度比较慢,I／O成为核外计算性能重要的限制因素．提出了一种使用运行库进行I／O优化的方法,给出了3种有效的优化策略：规则区域筛选、数据预取和边缘重用．编程人员可针对不同的应用问题使用相应的优化API来缩短程序执行时间．实验结果表明,通过减少I／O操作次数和内外存交换的数据量以及隐藏部分I／O操作延迟,有效提高了核外计算的性能．     

Scheduling algorithm based on prefetching in MapReduce clusters

Due to cluster resource competition and task scheduling policy, some map tasks are assigned to nodes without input data, which causes significant data access delay. Data locality is becoming one of the most critical factors to affect performance of MapReduce clusters. As machines in MapReduce clusters have large memory capacities, which are often underutilized, in-memory prefetching input data is an effective way to improve data locality. However, it is still posing serious challenges to cluster designers on what and when to prefetch. To effectively use prefetching, we have built HPSO (High Performance Scheduling Optimizer), a prefetching service based task scheduler to improve data locality for MapReduce jobs. The basic idea is to predict the most appropriate nodes for future map tasks based on current pending tasks and then preload the needed data to memory without any delaying on launching new tasks. To this end, we have implemented HPSO in Hadoop-1.1.2. The experiment results have shown that the method can reduce the map tasks causing remote data delay, and improves the performance of Hadoop clusters.     

并行文件系统中适度贪婪的Cache预取一体化算法

传统文件系统中的Ｃａｃｈｅ和预取技术是两种降低访问延迟的有效方法。在并行科学计算应用的Ｉ／Ｏ访问模式下,简单的Ｃａｃｈｅ和预取技术已无法提供较高的Ｃａｃｈｅ命中率,该文在分析该Ｉ／Ｏ模式的基础上提出了适度贪婪的Ｃａｃｈｅ和预取一体化算法（ＰＧＩ）,该算法充分利用了并行文件系统环境的特点,采用了适度贪婪的动态滑模技术,可以有铲地消除预取时的抖动,降低系统处理开锁,并同时采用了Ｃａｃｈｅ和预取一体化的     

并行数据库系统中并行I／O的研究与实现

并行Ｉ／Ｏ是基于无共享结构的并行数据库系统提高性能的有效途径之一。它通过并行磁盘服务和网络传输并行化提供了高带宽Ｉ／Ｏ。文章设计实现了基于无共享结构的并行数据库系统的并行Ｉ／Ｏ,探讨了设计并行Ｉ／Ｏ时的几个关键问题及实现技术。     

A Simple and Efficient Parallel Disk Mergesort

External sorting—the process of sorting a file that is too large to fit into the computer's internal memory and must be stored externally on disks—is a fundamental subroutine in database systems[G], [IBM]. Of prime importance are techniques that use multiple disks in parallel in order to speed up the performance of external sorting. The simple randomized merging (SRM ) mergesort algorithm proposed by Barve et al. [BGV] is the first parallel disk sorting algorithm that requires a provably optimal number of passes and that is fast in practice. Knuth [K,Section 5.4.9] recently identified SRM (which he calls ``randomized striping') as the method of choice for sorting with parallel disks. In this paper we present an efficient implementation of SRM, based upon novel and elegant data structures. We give a new implementation for SRM's lookahead forecasting technique for parallel prefetching and its forecast and flush technique for buffer management. Our techniques amount to a significant improvement in the way SRM carries out the parallel, independent disk accesses necessary to read blocks of input runs efficiently during external merging. Our implementation is based on synchronous parallel I/O primitives provided by the TPIE programming environment[TPI]; whenever our program issues an I/O read (write) operation, one block of data is synchronously read from (written to) each disk in parallel. We compare the performance of SRM over a wide range of input sizes with that of disk-striped mergesort (DSM ), which is widely used in practice. DSM consists of a standard mergesort in conjunction with striped I/O for parallel disk access. SRM merges together significantly more runs at a time compared with DSM, and thus it requires fewer merge passes. We demonstrate in practical scenarios that even though the streaming speeds for merging with DSM are a little higher than those for SRM (since DSM merges fewer runs at a time), sorting using SRM is often significantly faster than with DSM (since SRM requires fewer passes). The techniques in this paper can be generalized to meet the load-balancing requirements of other applications using parallel disks, including distribution sort and multiway partitioning of a file into several other files. Since both parallel disk merging and multimedia processing deal with streams that get ``consumed' at nonuniform and partially predictable rates, our techniques for lookahead based upon forecasting data may have relevance in video server applications. Received June 28, 2000, and in revised form June 5, 2001. Online publication April 8, 2002.     

Optimal Read-Once Parallel Disk Scheduling

An optimal prefetching and I/O scheduling algorithm L-OPT, for parallel I/O systems, using a read-once model of block references is presented. The algorithm uses knowledge of the next $L$ references, $L$-block lookahead, to create a minimal-length I/O schedule. For a system with $D$ disks and a buffer of capacity $m$ blocks, we show that the competitive ratio of L-OPT is $\Theta(\sqrt{mD/L})$ when $L \geq m$, which matches the lower bound of any prefetching algorithm with $L$-block lookahead. Tight bounds for the remaining ranges of lookahead are also presented. In addition we show that L-OPT is the optimal offline algorithm: when the lookahead consists of the entire reference string, it performs the absolute minimum possible number of I/Os. Finally, we show that L-OPT is comparable with the best online algorithm with the same amount of lookahead; the ratio of the length of its schedule to the length of the optimal schedule is always within a constant factor.     

一种新的自适应并行预取算法

研究了并行存储预取优化算法，根据并行存储的主要访问模式，提出要同时对文件内数据块访问和文件间访问进行建模，并对文件内数据块访问和文件间访问建模分别提出了E_IS_PPM算法和Last_N_Successor算法。最后将两个算法结合起来，提出了文件预取综合算法，算法根据计算和存储的可重叠程度以及文件预取页面的可获得性，自适应地决定预取深度。     

Scalable isosurface visualization of massive datasets on commodity off-the-shelf clusters

Tomographic imaging and computer simulations are increasingly yielding massive datasets. Interactive and exploratory visualizations have rapidly become indispensable tools to study large volumetric imaging and simulation data. Our scalable isosurface visualization framework on commodity off-the-shelf clusters is an end-to-end parallel and progressive platform, from initial data access to the final display. Interactive browsing of extracted isosurfaces is made possible by using parallel isosurface extraction, and rendering in conjunction with a new specialized piece of image compositing hardware called Metabuffer. In this paper, we focus on the back end scalability by introducing a fully parallel and out-of-core isosurface extraction algorithm. It achieves scalability by using both parallel and out-of-core processing and parallel disks. It statically partitions the volume data to parallel disks with a balanced workload spectrum, and builds I/O-optimal external interval trees to minimize the number of I/O operations of loading large data from disk. We also describe an isosurface compression scheme that is efficient for progress extraction, transmission and storage of isosurfaces.