In recent years, the rapid growth of the Internet has led to creation of massively large graphs. Since databases have become very large nowadays, they cannot be processed by a simple machine at an acceptable time anymore; therefore, traditional graph partitioning methods, which are often based on having a complete image of the entire graph, are not applicable to large datasets. This challenge has led to the appearance of a new approach called streaming graph partitioning. In streaming graph partitioning, a stream of input data is received by a partitioner, and partitioner decides which computational machine the data should be transferred to. Often, streaming partitioner does not have any information about the whole graph, and usually distributes the vertices based on some greedy heuristics which may not be optimal for incoming vertices. Hence, partitioner’s decision can be significantly improved if more information about the graph is utilized. In this paper, we present a new vertex-cut streaming graph partitioning approach. The proposed method uses the idea of postponing the decision for some of the edges (by means of an intelligent buffering) and corrects some of the past decisions to improve the quality of the graph partitioning. The proposed approach is evaluated using from real-world graphs. The experimental results show that the performance of the proposed method is superior in comparison with the previous HDRF method.

中文翻译：

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RBSEP：一种基于重新分配和缓冲区的流边缘划分方法

近年来，Internet的快速发展导致创建了巨大的图形。由于如今数据库已经变得非常庞大，因此无法再在可接受的时间内用简单的机器对其进行处理；因此，通常基于具有整个图的完整图像的传统图分区方法不适用于大型数据集。这一挑战导致出现了一种称为流图分区的新方法。在流图分区中，输入数据流由分区器接收，然后分区器决定应将数据传输至哪个计算机。流分割器通常没有关于整个图的任何信息，并且通常基于一些贪婪启发法来分配顶点，这对于传入的顶点可能不是最佳的。因此，如果利用有关图的更多信息，则可以显着改善分区程序的决策。在本文中，我们提出了一种新的顶点切割流图分割方法。所提出的方法使用了推迟某些边缘的决策（通过智能缓冲的方法）的想法，并纠正了一些过去的决策，以提高图划分的质量。所提出的方法是使用真实世界的图表进行评估的。实验结果表明，该方法的性能优于以前的HDRF方法。所提出的方法使用了推迟某些边缘的决策（通过智能缓冲的方法）的想法，并纠正了一些过去的决策，以提高图划分的质量。所提出的方法是使用真实世界的图表进行评估的。实验结果表明，该方法的性能优于以前的HDRF方法。所提出的方法使用了推迟某些边缘的决策（通过智能缓冲的方法）的想法，并纠正了一些过去的决策，以提高图划分的质量。所提出的方法是使用真实世界的图表进行评估的。实验结果表明，该方法的性能优于以前的HDRF方法。