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首页> 外文期刊>Journal of Grid Computing >IQ-Paths: Predictably High Performance Data Streams Across Dynamic Network Overlays
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IQ-Paths: Predictably High Performance Data Streams Across Dynamic Network Overlays

机译:IQ路径:动态网络覆盖范围内可预测的高性能数据流

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Overlay networks are a key vehicle for delivering network and processing resources to high performance applications. For shared networks, however, to consistently deliver such resources at desired levels of performance, overlays must be managed at runtime, based on the continuous assessment and prediction of available distributed resources. Data-intensive applications, for example, must assess, predict, and judiciously use available network paths, and dynamically choose alternate or exploit concurrent paths. Otherwise, they cannot sustain the consistent levels of performance required by tasks like remote data visualization, online program steering, and remote access to high end devices. The multiplicity of data streams occurring in complex scientific workflows or in large-scale distributed collaborations exacerbate this problem, particularly when different streams have different performance requirements. This paper presents IQ-Paths, a set of techniques and their middleware realization that implement self-regulating overlay streams for data-intensive distributed applications. Self-regulation is based on (1) the dynamic and continuous assessment of the quality of each overlay path, (2) the use of online network monitoring and statistical analyses that provide probabilistic guarantees about available path bandwidth, loss rate, and RTT, and (3) self-management, via an efficient packet routing and scheduling algorithm that dynamically schedules data packets to different overlay paths in accordance with their available bandwidths. IQ-Paths offers probabilistic guarantees for application-level specifications of stream utility, based on statistical predictions of available network bandwidth. This affords applications with the ability, for instance, to send control or steering data across overlay paths that offer strong guarantees for future bandwidth vs. across less guaranteed paths. Experimental results presented in this paper use IQ-Paths to better handle the different kinds of data produced by two high performance applications and one multimedia application: (1) a data-driven interactive high performance code with user-defined utility requirements, (2) an adaptive overlay version of the popular Grid-FTP application, and (3) a MPEG-4 Fine-Grained Scalable layered video streaming.
机译:覆盖网络是用于向高性能应用程序交付网络和处理资源的关键工具。但是,对于共享网络,要始终如一地以所需的性能级别交付此类资源,必须基于对可用分布式资源的连续评估和预测,在运行时管理覆盖。例如,数据密集型应用程序必须评估,预测和明智地使用可用的网络路径,并动态选择备用或利用并发路径。否则,它们将无法维持远程数据可视化,在线程序控制和远程访问高端设备等任务所需的一致性能水平。复杂的科学工作流或大规模分布式协作中出现的大量数据流加剧了此问题,尤其是在不同的流具有不同的性能要求时。本文介绍了IQ-Paths,这是一套技术及其中间件实现,可为数据密集型分布式应用程序实现自我调节的覆盖流。自我调节的基础是(1)动态并连续评估每个覆盖路径的质量,(2)使用在线网络监视和统计分析来提供有关可用路径带宽,丢失率和RTT的概率保证,以及(3)通过有效的数据包路由和调度算法进行自我管理,该算法根据数据包的可用带宽将数据包动态调度到不同的覆盖路径。 IQ-Paths基于对可用网络带宽的统计预测,为流实用程序的应用程序级别规范提供了概率保证。例如,这使应用程序能够跨覆盖路径发送控制或控制数据,从而为将来的带宽提供了有力保证,而跨覆盖较少的路径则提供了有力的保证。本文介绍的实验结果使用IQ-Paths来更好地处理两个高性能应用程序和一个多媒体应用程序生成的各种数据:(1)具有用户定义的实用程序要求的数据驱动的交互式高性能代码,(2)流行的Grid-FTP应用程序的自适应覆盖版本,以及(3)MPEG-4细粒度可扩展分层视频流。

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