Graph partitioning is a mandatory step in large-scale distributed graph processing. When partitioning real-world power-law graphs, the edge partitioning algorithm performs better than the traditional vertex partitioning algorithm, because it can cut a single vertex into multiple replicas to apportion the computation. Many advanced edge partitioning methods are designed for partitioning a static graph from scratch. However, the real-world graph structure changes continuously, which leads to a decrease in partition quality and affects the performance of the graph applications. Some studies are devoted to offline repartitioning or batch incremental partitioning, but how to deal with dynamics in real-time is still worthy of in-depth study. In this article, we discuss the impact of dynamic change on partition and discover that both insertion and deletion will lead to local suboptimal partitioning, which is the reason for the degradation of partition quality. As a solution, a dynamic edge partitioning algorithm is proposed to partition dynamics in real-time. Specifically, we deal with dynamics by a distributed stream and improve partition quality by reassigning some closely connected edges. Experiments show that it is robust to initial partition quality, dynamic scale and type, and distributed scale. Compared with the state-of-the-art dynamic partitioner, it can reduce vertex-cuts by 29.5 percent. Compared with the repartitioning algorithms, it can save the partitioning time by 91.0 percent. Applied on the graph task, it can reduce the increase of communication cost and the increase of the total time of task by 41.5 and 71.4 percent.

中文翻译：

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动态边缘分区的组重新分配

在大规模分布式图形处理中，图形分区是必不可少的步骤。在对现实世界的幂律图进行分区时，边缘分区算法的性能要优于传统的顶点分区算法，因为它可以将单个顶点切成多个副本以分配计算量。许多先进的边缘分区方法都设计用于从头开始对静态图进行分区。但是，现实世界中的图形结构不断变化，这导致分区质量下降，并影响图形应用程序的性能。一些研究致力于脱机重新分区或批处理增量分区，但是如何实时处理动态仍然值得深入研究。在本文中，我们讨论了动态变化对分区的影响，发现插入和删除都会导致局部次优分区，这是分区质量下降的原因。作为一种解决方案，提出了一种动态边缘分割算法来对动态区域进行实时分割。具体来说，我们通过分布式流来处理动态，并通过重新分配一些紧密相连的边缘来提高分区质量。实验表明，它对初始分区质量，动态规模和类型以及分布式规模具有鲁棒性。与最新的动态分区器相比，它可以减少29.5％的顶点切割。与重新划分算法相比，它可以节省91.0％的划分时间。应用于图任务