具有数据感知桌面网格中间件的BLAST应用程序

Haiwu He, G. Fedak, B. Tang, F. Cappello
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引用次数: 18

摘要

有许多网格中间件可以在计算网格上开发和执行程序,但是它们仍然需要用户进行大量的工作。BitDew旨在为需要大量数据处理的应用程序提供具有动态、异构、易失性和高度分布式计算资源的大规模网格。数据密集型应用构成了电子科学社区的重要应用类别,它需要安全协调地访问大型数据集、广域传输和tb级数据的广泛分发,同时保持对多个数据副本的跟踪。在遗传生物学中,基因序列比较和分析是最基本的程序。随着需要分析的序列的大量增加,我们需要越来越强的计算能力以及高效的数据管理解决方案。在这项工作中,我们研究了使用新的桌面网格中间件BitDew的优势,BitDew是为大规模数据管理而设计的。我们的贡献是双重的:首先,我们引入了一个数据驱动的主/从编程模型,并根据该模型在BitDew上提出了BLAST的实现,其次,我们提出了广泛的实验和仿真结果,证明了我们方法的有效性和可扩展性。我们评估了多协议数据分布实现显著提速的好处,我们报告了处理相对性能下降的高度易变环境的能力,我们展示了网格中具有异构资源性能的数据复制的好处,我们评估了在易变资源上计算时数据容错和数据复制的组合。
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BLAST Application with Data-Aware Desktop Grid Middleware
There exists numerous Grid middleware to develop and execute programs on the computational Grid, but they still require intensive work from their users. BitDew is made to facilitate the usage of large scale Grid with dynamic, heterogeneous, volatile and highly distributed computing resources for applications that require a huge amount of data processing. Data-intensive applications form an important class of applications for the e-Science community which require secure and coordinated access to large datasets, wide-area transfers and broad distribution ofTeraBytes of data while keeping track of multiple data replicas. In genetic biology, gene sequences comparison and analysis are the most basic routines. With the considerable increase of sequences to analyze, we need more and more computing power as well as efficient solution to manage data. In this work, we investigate the advantages of using a new Desktop Grid middleware BitDew, designed for large scale data management.Our contribution is two-fold: firstly, we introduce a data-driven Master/Slave programming model and we present an implementation of BLAST over BitDew following this model, secondly, we present extensive experimental and simulation results which demonstrate the effectiveness and scalability of our approach. We evaluate the benefit of multi-protocol data distribution to achieve remarkable speedups, we report on the ability to cope with highly volatile environment with relative performance degradation, we show the benefit of data replication in Grid with heterogeneous resource performance and we evaluate the combination of data fault tolerance and data replication when computing on volatileresources.
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