Inverted file partitioning schemes in multiple disk systems

Multiple-disk I/O systems (disk arrays) have been an attractive approach to meet high performance I/O demands in data intensive applications such as information retrieval systems. When we partition and distribute files across multiple disks to exploit the potential for I/O parallelism, a balanced I/O workload distribution becomes important for good performance. Naturally, the performance of a parallel information retrieval system using an inverted file structure is affected by the partitioning scheme of the inverted file. In this paper, we propose two different partitioning schemes for an inverted file system for a shared-everything multiprocessor machine with multiple disks. We study the performance of these schemes by simulation under a number of workloads where the term frequencies in the documents are varied, the term frequencies in the queries are varied, the number of disks are varied and the multiprogramming level is varied     

Architectural Effects of Symmetric Multiprocessors on TPC-C Commercial Workload

Commercial transaction processing applications are an important workload running on symmetric multiprocessor systems (SMPs). They differ dramatically from scientific, numeric-intensive, and engineering applications because they are I/O bound, and they contain more system software activities. Most SMP servers available in the market have been designed and optimized for scientific and engineering workloads. A major challenge of studying architectural effects on the performance of a commercial workload is the lack of easy access to large-scale and complex database engines running on a multiprocessor system with powerful I/O facilities. Experiments involving case studies have been shown to be highly time-consuming and expensive. In this paper, we investigate the feasibility of using queuing network models with the support of simulation to study the SMP architectural impacts on the performance of commercial workloads. We use the commercial benchmark TPC-C as the workload. A bus-based SMP machine is used as the target platform. Queueing network modeling is employed to characterize the TPC-C workload on the SMP. The system components such as processors, memory, the memory bus, I/O buses, and disks are modeled as service centers, and their effects on performance are analyzed. Simulations are conducted as well to collect the workload-specific parameters (model parameterization) and to verify the accuracy of the model. Our studies find that among disk-related parameters, the disk rotation latency affects the performance of TPC-C most significantly. Among I/O buses and number of disks, the number of I/O buses has the deepest impact on performance. This study also demonstrates that our modeling approach is feasible, cost-effective, and accurate for evaluating the performance of commercial workloads on SMPs, and it is complementary to the measurement-based experimental approaches.     

Exploring optimal combination of a file system and an I/O scheduler for underlying solid state disks

Performance and energy consumption of a solid state disk （SSD） highly depend on file systems and I/O schedulers in operating systems. To find an optimal combination of a file system and an I/O scheduler for SSDs, we use a metric called the aggregative indicator （AI）, which is the ratio of SSD performance value （e.g., data transfer rate in MB/s or throughput in IOPS） to that of energy consumption for an SSD. This metric aims to evaluate SSD performance per energy consumption and to study the SSD which delivers high performance at low energy consumption in a combination of a file system and an I/O scheduler. We also propose a metric called Cemp to study the changes of energy consumption and mean performance for an Intel SSD （SSD-I） when it provides the largest AI, lowest power, and highest performance, respectively. Using Cemp, we attempt to find the combination of a file system and an I/O scheduler to make SSD-I deliver a smooth change in energy consumption. We employ Filebench as a workload generator to simulate a wide range of workloads （i.e., varmail, fileserver, and webserver）, and explore optimM combinations of file systems and I/O schedulers （i.e., optimal values of AI） for tested SSDs under different workloads. Experimental results reveal that the proposed aggregative indicator is comprehensive for exploring the optimal combination of a file system and an I/O scheduler for SSDs, compared with an individual metric.     

一种基于虚拟机的高效磁盘I/O特征分析方法

于磁盘系统的机械运动本质,磁盘系统I/O往往会成为计算机系统的性能瓶颈.为了有效地提高系统性能,收集和分析应用系统的磁盘I/O特征信息将成为性能优化工作的重要基础.与以往I/O特征分析方法不同,给出了一种基于Xen 3.0虚拟机系统的磁盘I/O特征在线分析方法.在虚拟机环境下,该磁盘I/O特征采集方法可以透明地应用于任意无须修改的操作系统.该方法可以高效地在线采集多种基本I/O特征数据,其中包括:磁盘I/O块大小、I/O延迟、I/O时间间隔、I/O空间局部性、时间局部性以及磁盘I/O操作热点分布.通过测试和分析,该在线I/O分析方法有着较小的系统开销,并且对应用系统I/O性能的影响很小.此外,还给出了在大文件拷贝、基于Filebench的filemirco和varmail等工作负载下的I/O特征分析结果.     

基于RAM/Disk混合设备模型的FC-SAN存储系统

磁盘存取是基于光纤通道网络的SAN存储系统的目前性能瓶径，在综合和分析目前各种文件系统I／O操作工作负载的研究结果的基础上，提出了一个新的改进FC-SAN存储系统性能的方法：将各种文件系统I／O操作分为大数据量的文件读写操作、小数据量的文件读写操作和文件属性操作，大数据量的文件读写操作还是按照原来的I／O路径进行，存取物理磁盘；但其他各种文件操作包括小数据量的文件读写操作对基于内存的RAMDisk设备进行操作，实验结果显示，基于混合I／O子系统的FC-SAN存储系统的存取速率可以接近线速。     

一个提高机群文件系统吞吐率的方法及其实现

随着工作站机群系统的迅猛发展,I/O瓶颈问题也日益严重。性能优良的机群文件系统对机群系统而言至关重要。吞吐率是指在单位时间内所处理的作业数,它是评价系统性能的一个重要指标。论文提出用路径名的多成分查找来提高文件系统的创建删除吞吐率,给出并在PVFS上实现了一个路径名多成分查找方法。文章的最后对PVFS进行了优化前后的性能比较,并给出了自己的结论。     

Accelerating big data analytics on HPC clusters using two-level storage

Data-intensive applications that are inherently I/O bound have become a major workload on traditional high-performance computing (HPC) clusters. Simply employing data-intensive computing storage such as HDFS or using parallel file systems available on HPC clusters to serve such applications incurs performance and scalability issues. In this paper, we present a novel two-level storage system that integrates an upper-level in-memory file system with a lower-level parallel file system. The former renders memory-speed high I/O performance and the latter renders consistent storage with large capacity. We build a two-level storage system prototype with Tachyon and OrangeFS, and analyze the resulting I/O throughput for typical MapReduce operations. Theoretical modeling and experiments show that the proposed two-level storage delivers higher aggregate I/O throughput than HDFS and OrangeFS and achieves scalable performance for both read and write. We expect this two-level storage approach to provide insights on system design for big data analytics on HPC clusters.     

一种自适应负载的I/O调度算法

I/O调度算法对磁盘阵列(RAID)性能具有至关重要的影响。虽然已有很多典型的I/O调度算法在一定负载情况下可获得较好的性能,但很难有哪一种算法在各种负载情况下均能获得很好的性能。本文提出了一种智能RAID控制模型,结合C4.5决策树和AdaBoost算法实现负载自动分类,根据负载变化和性能反馈情况动态调整I/O调度策略,实现面向应用需求的自治调度。模拟实验结果表明,自适应调度算法具有较好的适应性,在各种负载情况下优于现有的I/O调度算法,尤其适用于多线程混合负载环境的I/O性能优化。     

I/O负载自相似研究综述

研究I/O负载特征,建立恰当的I/O负载模型,对存储系统的设计、优化和评估有重要的意义.通过研究发现,许多I/O到达模式和访问模式在不同尺度下都具有相似的"突发性".而传统的泊松模型不具有这种特点,因此,研究者提出用自相似模型来描述I/O负载.此外,虽然自相似模型能够较好地描述I/O负载的长相关性,但是,它们却难以描述较小尺度上的局部奇异行为.于是,一些研究者提出用多分形模型来弥补这一缺陷.总结了目前I/O负载自相似研究的成果,分析了I/O负载自相似的研究现状,探讨了I/O负载自相似研究的发展趋势.通过分析得出,目前,自相似的评估问题还未被完全解决,I/O负载的自相似性在存储系统设计中的意义的研究还需要更深入,另外,如何建立更准确高效的I/O负载多分形模型也是一个还需要更深入研究的方面.     

A light-weight,collaborative temporary file system for clustered Web servers

Previous studies indicate that I/O could become a performance bottleneck in commodity PC-based cluster Web servers. Current local native file systems do not work well for expensive file I/Os while specialized file systems have a limitation on portability. In this paper, we present a lightweight, collaborative temporary file system (CTFS) to improve disk I/O performance for clustered Web servers. CTFS employs several techniques to achieve high-performance, good scalability and portability: (1) a lightweight local temporal file system at each node, (2) using Remote Direct Memory Access (RDMA) to improve intra-cluster communication performance, and (3) a location-aware summary cache for scalable file-to-server lookup. Comprehensive trace-driven simulation experiments conclude that CTFS achieves up to a 37% better system throughput and reduces up to 47% total disk I/O latency than a local asynchronous FFS solution.     

高性能集群文件系统的研究

集群系统提供了强大的批处理和并行计算的能力,具有高性能、高可扩展性、高吞吐量和易用性等特点,但是I／O性能和处理器性能的不匹配使得I／O成为许多应用的瓶颈,特别是处理大量数据的应用就更是如此。针对集群系统当前的现状,克服该瓶颈的常用方法就是采用一种并行虚拟文件系统（PVFS）技术。随着Linux群集系统性能的持续提高,高速并行文件已成为并行计算的一个必备部分。并行虚拟文件系统（PVFS）为高性能计算（HPC）群集和大型I／O密集并行应用提供了这样一个文件系统。首先介绍了PVFS的结构;然后研究了PVFS的存取和管理机制;最后分析,PVFS的工作原理。     

RPM: a rapid prototyping engine for multiprocessor systems

RPM enables rapid prototyping of different multiprocessor architectures. It uses hardware emulation for reliable design verification and performance evaluation. The major objective of the RPM project is to develop a common, configurable hardware platform to accurately emulate different MIMD systems with up to eight execution processors. Because emulation is orders of magnitude faster than simulation, an emulator can run problems with large data sets more representative of the workloads for which the target machine is designed. Because an emulation is closer to the target implementation than an abstracted simulation, it can accomplish more reliable performance evaluation and design verification. Finally, an emulator is a real computer with its own I/O; the code running on the emulator is not instrumented. As a result, the emulator looks exactly like the target machine (to the programmer) and can run several different workloads, including code from production compilers, operating systems, databases, and software utilities     

一种高性能I／O方法

并存文伴系统是解决I／O瓶颈问题的重要途径。研究表明，科学应用中跨越式的文件访问模式与现存并行文件系统访问这些数据的方法的结合，对于大型数据集的访问其I／O性能是难以接受的。为了提高并行文件系统中对不连续数据的I／O性能，创建了一种新型高性能I／O方法：用户自定义文件视图结合合并I／O请求。并且在WPFS并行文件系统中实现了该方法。研究和实验结果表明，该方法具有增强科学应用性能的潜力。     

Self-Learning Disk Scheduling

Performance of disk I/O schedulers is affected by many factors, such as workloads, file systems, and disk systems. Disk scheduling performance can be improved by tuning scheduler parameters, such as the length of read timers. Scheduler performance tuning is mostly done manually. To automate this process, we propose four self-learning disk scheduling schemes: Change-sensing Round-Robin, Feedback Learning, Per-request Learning, and Two-layer Learning. Experiments show that the novel Two-layer Learning Scheme performs best. It integrates the workload-level and request-level learning algorithms. It employs feedback learning techniques to analyze workloads, change scheduling policy, and tune scheduling parameters automatically. We discuss schemes to choose features for workload learning, divide and recognize workloads, generate training data, and integrate machine learning algorithms into the Two-layer Learning Scheme. We conducted experiments to compare the accuracy, performance, and overhead of five machine learning algorithms: Decision Tree, Logistic Regression, Naïve Bayes, Neural Network, and Support Vector Machine Algorithms. Experiments with real-world and synthetic workloads show that self-learning disk scheduling can adapt to a wide variety of workloads, file systems, disk systems, and user preferences. It outperforms existing disk schedulers by as much as 15.8% while consuming less than 3%-5% of CPU time.     

Design, analysis, and simulation of I/O architectures for hypercubemultiprocessors

Several issues concerning the design of an I/O (input/output) system for a multiprocessor such as a hypercube are examined. A methodology is proposed for connecting the I/O processors to such a system for efficient I/O access. The effect of I/O communication on the multiprocessor network is analyzed. Different disk organizations that can be employed within such a system are evaluated to see which organization has a better performance. It is observed that parallelism in serving an I/O request plays a dominant role in the scientific workload. The problem of mapping specific data structures such as matrices onto the disks so that the data can be accessed efficiently is considered     

An experimental study of relative throughput in a multiprocessor computer system

This paper describes the results of controlled experiments with a Honeywell H6000 series multiprocessor computer system, 212 experiments were performed using 14 test workloads on 8 H6000 configurations. The number of processors varied from 1 to 4, system controller units (SCUs) from 1 to 4, and main memory from 256K to 1024K words. The ratio (P) of I/O time and CPU time for the test workloads varied from 0.01 to 5.07. The improvement in throughput is expressed in terms of relative throughput (φ), defined as the ratio of elapsed time for a given test workload on a single-processor configuration to that on a multiprocessor configuration. The relative throughput increased monotonically with an increase in the number of processors for test workloads in the range 0<P<0.4 (CPU-bound) and φ exhibited an asymptotic behaviour for test workloads in the range 0.4<P<5.07 (I/O-bound).     

QMDS: a file system metadata management service supporting a graph data model-based query language

File system metadata management has become a bottleneck for many data-intensive applications that rely on high-performance file systems. Part of the bottleneck is due to the limitations of an almost 50-year-old interface standard with metadata abstractions that were designed at a time when high-end file systems managed less than 100 MB. Today's high-performance file systems store 7–9 orders of magnitude more data, resulting in a number of data items for which these metadata abstractions are inadequate, such as directory hierarchies unable to handle complex relationships among data. Users of file systems have attempted to work around these inadequacies by moving application-specific metadata management to relational databases to make metadata searchable. Splitting file system metadata management into two separate systems introduces inefficiencies and systems management problems. To address this problem, we propose QMDS: a file system metadata management service that integrates all file system metadata and uses a graph data model with attributes on nodes and edges. Our service uses a query language interface for file identification and attribute retrieval. We present our metadata management service design and architecture and study its performance using a text analysis benchmark application. Results from our QMDS prototype show the effectiveness of this approach. Compared to the use of a file system and relational database, the QMDS prototype shows superior performance for both ingest and query workloads.     

Prefetching in file systems for MIMD multiprocessors

The question of whether prefetching blocks on the file into the block cache can effectively reduce overall execution time of a parallel computation, even under favorable assumptions, is considered. Experiments have been conducted with an interleaved file system testbed on the Butterfly Plus multiprocessor. Results of these experiments suggest that (1) the hit ratio, the accepted measure in traditional caching studies, may not be an adequate measure of performance when the workload consists of parallel computations and parallel file access patterns, (2) caching with prefetching can significantly improve the hit ratio and the average time to perform an I/O (input/output) operation, and (3) an improvement in overall execution time has been observed in most cases. In spite of these gains, prefetching sometimes results in increased execution times (a negative result, given the optimistic nature of the study). The authors explore why it is not trivial to translate savings on individual I/O requests into consistently better overall performance and identify the key problems that need to be addressed in order to improve the potential of prefetching techniques in the environment     

I/O受限的并行加速比模型与可扩展I/O体系结构

为了缓解I/O瓶颈问题,可以从应用程序、可扩展算法、编译器和语言、运行时库、操作系统和体系结构六方面展开研究。其中,I/O体系结构是所有技术途径的关键支撑。当前并行I/O性能分析缺乏科学的理论模型为I/O体系结构设计提供理论依据。本文针对并行计算机系统的可扩展性问题,研究了I/O负载对并行计算机系统可扩展性的影响,建立了I/O受限的并行加速比性能模型,对目前大规模并行计算机系统中三种常用I/O体系结构的可扩展性进行了分析;以此为理论依据,提出了一种面向高性能计算的可扩展并行I/O系统结构。同时,还提出了几种有效降低I/O操作服务时间的策略,从而达到增强系统可扩展性的目的,为后续研究奠定了基础。     

FAIRIO: A Throughput-oriented Algorithm for Differentiated I/O Performance

Providing differentiated service in a consolidated storage environment is a challenging task. To address this problem, we introduce FAIRIO, a cycle-based I/O scheduling algorithm that provides differentiated service to workloads concurrently accessing a consolidated RAID storage system. FAIRIO enforces proportional sharing of I/O service through fair scheduling of disk time. During each cycle of the algorithm, I/O requests are scheduled according to workload weights and disk-time utilization history. Experiments, which were driven by the I/O request streams of real and synthetic I/O benchmarks and run on a modified version of DiskSim, provide evidence of FAIRIO’s effectiveness and demonstrate that fair scheduling of disk time is key to achieving differentiated service in a RAID storage system. In particular, the experimental results show that, for a broad range of workload request types, sizes, and access characteristics, the algorithm provides differentiated storage throughput that is within 10% of being perfectly proportional to workload weights; and, it achieves this with little or no degradation of aggregate throughput. The core design concepts of FAIRIO, including service-time allocation and history-driven compensation, potentially can be used to design I/O scheduling algorithms that provide workloads with differentiated service in storage systems comprised of RAIDs, multiple RAIDs, SANs, and hypervisors for Clouds.