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Placing big graph into cloud for parallel processing with a two-phase community-aware approach

机译：使用两阶段社区感知方法将大图放入云中进行并行处理

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摘要

Big graphs are so large that their analysis often rely on the cloud for parallel processing. Data placement, as a key pre-processing step, has a profound impact on the performance of parallel processing. Traditional placement methods fail to preserve graph topologies, leading to poor performance. As the community is the most common structure of big graphs, in this work, we present a two-phase community-aware placement algorithm to place big graphs into the cloud for parallel processing. It can obtain a placement scheme that preserves the community structure well by maximizing the modularity density of the scheme under memory capacity constraints of computational nodes of the cloud in two phases. In the first phase, we design a streaming partitioning heuristic to detect communities based on partial and incomplete graph information. They form an initial placement scheme with relatively high modularity density. To improve it further, in the second phase, we put forward a scale-constrained kernel k-means algorithm. It takes as input the initial placement scheme and iteratively redistributes graph vertices across computational nodes under scale constraints until the modularity density cannot be improved any further. Finally, experiments show that our algorithm can preserve graph topologies well and greatly support parallel processing of big graphs in the cloud. (C) 2019 Elsevier B.V. All rights reserved.
机译：大图是如此之大，以至于其分析经常依赖于云进行并行处理。数据放置作为关键的预处理步骤，对并行处理的性能产生深远的影响。传统的放置方法无法保留图形拓扑，从而导致性能不佳。由于社区是大图的最常见结构，因此在本文中，我们提出了一种两阶段的社区感知放置算法，将大图放置到云中以进行并行处理。它可以通过在两个阶段的云计算节点的存储容量约束下最大化方案的模块密度来获得一种能够很好地保留社区结构的布局方案。在第一阶段，我们设计了一种流分区启发法，以基于部分和不完整的图信息检测社区。它们形成具有较高模块密度的初始放置方案。为了进一步改进它，在第二阶段，我们提出了一个尺度受限的核k均值算法。它以初始放置方案为输入，并在比例约束下跨计算节点迭代地重新分布图顶点，直到无法进一步提高模块密度为止。最后，实验表明我们的算法可以很好地保留图拓扑，并极大地支持云中大图的并行处理。 （C）2019 Elsevier B.V.保留所有权利。

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