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首页> 外文期刊>International Journal of High Performance Computing and Networking >A novel graph compression algorithm for data-intensive scientific networks
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A novel graph compression algorithm for data-intensive scientific networks

机译:一种新型数据密集型科学网络的绘图压缩算法

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摘要

As one of the world's leading scientific and data-intensive computing grids, the worldwide LHC computing grid (WLCG) faces the challenge of improving its computing efficiency and network utilisation. To achieve this goal, WLCG needs an important piece of information: the network topology graphs of participating computing grids. Directly collecting such information from all of the grids, however, would cause high communication overhead and raise many security issues. In this paper, we address these issues by proposing a novel algorithm to compress such a large network topology into a compact, equivalent network topology. We formally define our problem, develop a novel, efficient topology compression algorithm and evaluate its performance using real-world network topologies. Our results show that our algorithm not only achieves a much higher topology compression ratio than state-of-the-art topology transformation algorithms, but also leads to at most 100× reduction in computation time.
机译:作为世界领先的科学和数据密集型计算网格之一,全球LHC计算网格(WLCG)面临提高其计算效率和网络利用的挑战。 为了实现这一目标,WLCG需要一个重要的信息:参与计算网格的网络拓扑图。 然而,直接从所有网格中收集此类信息会导致高通信开销并提高许多安全问题。 在本文中,我们通过提出一种新颖的算法将这种大型网络拓扑压缩成紧凑,等效的网络拓扑结构来解决这些问题。 我们正式定义了我们的问题,开发了一种新颖,高效的拓扑压缩算法,并使用真实网络拓扑评估其性能。 我们的结果表明,我们的算法不仅可以实现比最先进的拓扑变换算法更高的拓扑压缩比,而且还导致计算时间最多100倍。

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