GatorShare:用于高吞吐量数据管理的文件系统框架

Jiangyan Xu, R. Figueiredo
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引用次数: 10

摘要

自愿计算系统或桌面网格(dg)使全球范围内的商品计算资源共享成为可能,并在科学研究社区中获得了极大的普及。数据管理是在大型数据密集型应用程序中采用自愿计算范式的主要挑战之一。迄今为止,用于支持此类应用程序的中间件要么缺乏有效的协作数据分发方案,要么由于需要使用特定于中间件的api,无法轻松容纳现有的数据密集型应用程序。为了应对这一挑战,本文介绍了Gator-Share,这是一个数据管理框架,它提供了一个文件系统接口和一个可扩展的架构,旨在支持多种数据传输协议,包括BitTorrent,我们在此基础上实现了dg的协作数据分发服务。它简化了与桌面网格的集成,并为未修改的数据密集型应用程序提供了高吞吐量数据管理。为了提高BitTorrent在桌面网格中的性能,我们增强了BitTorrent,使其完全去中心化,并能够以按需方式支持部分文件下载。为了证明这种方法的合理性,我们从数据分发效率的角度对框架进行了定量评估。实验结果表明,与传统的客户端/服务器架构相比,该框架显著提高了未修改数据密集型应用程序的数据分发性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
GatorShare: a file system framework for high-throughput data management
Voluntary Computing systems or Desktop Grids (DGs) enable sharing of commodity computing resources across the globe and have gained tremendous popularity among scientific research communities. Data management is one of the major challenges of adopting the Voluntary Computing paradigm for large data-intensive applications. To date, middleware for supporting such applications either lacks an efficient cooperative data distribution scheme or cannot easily accommodate existing data-intensive applications due to the requirement for using middleware-specific APIs. To address this challenge, in this paper we introduce Gator-Share, a data management framework that offers a file system interface and an extensible architecture designed to support multiple data transfer protocols, including BitTorrent, based on which we implement a cooperative data distribution service for DGs. It eases the integration with Desktop Grids and enables high-throughput data management for unmodified data-intensive applications. To improve the performance of BitTorrent in Desktop Grids, we have enhanced BitTorrent by making it fully decentralized and capable of supporting partial file downloading in an on-demand fashion. To justify this approach we present a quantitative evaluation of the framework in terms of data distribution efficiency. Experimental results show that the framework significantly improves the data dissemination performance for unmodified data-intensive applications compared to a traditional client/server architecture.
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