Extensible optimization in overlay dissemination trees

Olga Papaemmanouil, Yanif Ahmad, U. Çetintemel, John Jannotti, Y. Yildirim
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引用次数: 34

Abstract

We introduce XPORT, a profile-driven distributed data dissemination system that supports an extensible set of data types, profile types, and optimization metrics. XPORT efficiently implements a generic tree-based overlay network, which can be customized per application using a small number of methods that encapsulate application-specific data filtering, profile aggregation, and optimization logic. The clean separation between the "plumbing" and "application" enables the system to uniformly support disparate dissemination-based applications.We first provide an overview of the basic XPORT model and architecture. We then describe in detail an extensible optimization framework, based on a two-level aggregation model, that facilitates easy specification of a wide range of commonly used performance goals. We discuss distributed tree transformation protocols that allow XPORT to iteratively optimize its operation to achieve these goals under changing network and application conditions. Finally, we demonstrate the flexibility and the effectiveness of XPORT using real-world data and experimental results obtained from both prototype-based LAN emulation and deployment on PlanetLab.
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覆盖传播树的可扩展优化
我们介绍XPORT,这是一个概要文件驱动的分布式数据传播系统,它支持一组可扩展的数据类型、概要文件类型和优化指标。XPORT有效地实现了一个通用的基于树的覆盖网络,可以使用少量方法对每个应用程序进行定制,这些方法封装了特定于应用程序的数据过滤、配置文件聚合和优化逻辑。“管道”和“应用程序”之间的清晰分离使系统能够统一地支持不同的基于传播的应用程序。我们首先概述基本的XPORT模型和体系结构。然后,我们详细描述了一个基于两级聚合模型的可扩展优化框架,该框架可以方便地规范广泛的常用性能目标。我们讨论了允许XPORT在不断变化的网络和应用条件下迭代优化其操作以实现这些目标的分布式树转换协议。最后,我们使用基于原型的局域网仿真和PlanetLab部署的实际数据和实验结果来演示XPORT的灵活性和有效性。
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