Large-Scale Distributed Graph Computing Systems: An Experimental Evaluation

With the prevalence of graph data in real-world applications (e.g., social networks, mobile phone networks, web graphs, etc.) and their ever-increasing size, many distributed graph computing sys-tems have been developed in recent years to process and analyze massive graphs. Most of these systems adopt Pregel’s vertex-centric computing model, while various techniques have been proposed to address the limitations in the Pregel framework. However, there is a lack of comprehensive comparative analysis to evaluate the perfor-mance of various systems and their techniques, making it difficult for users to choose the best system for their applications. We con-duct extensive experiments to evaluate the performance of existing systems on graphs with different characteristics and on algorithms with different design logic. We also study the effectiveness of var-ious techniques adopted in existing systems, and the scalability of the systems. The results of our study reveal the strengths and limi-tations of existing systems, and provide valuable insights for users, researchers and system developers. 1