Efficient data partitioning model for heterogeneous graphs in the cloud

Abstract

As the size and variety of information networks continue to grow in many scientific and engineering domains, we witness a growing demand for efficient processing of large heterogeneous graphs using a cluster of compute nodes in the Cloud. One open issue is how to effectively partition a large graph to process complex graph operations efficiently. In this paper, we present VB-Partitioner - a distributed data partitioning model and algorithms for efficient processing of graph operations over large-scale graphs in the Cloud. Our VB-Partitioner has three salient features. First, it introduces vertex blocks (VBs) and extended vertex blocks (EVBs) as the building blocks for semantic partitioning of large graphs. Second, VB-Partitioner utilizes vertex block grouping algorithms to place those vertex blocks that have high correlation in graph structure into the same partition. Third, VB-Partitioner employs a VB-partition guided query partitioning model to speed up the parallel processing of graph pattern queries by reducing the amount of inter-partition query processing. We conduct extensive experiments on several real-world graphs with millions of vertices and billions of edges. Our results show that VB-Partitioner significantly outperforms the popular random block-based data partitioner in terms of query latency and scalability over large-scale graphs.