Profiling distributed graph processing systems through visual analytics

Analyzing large-scale graphs provides valuable insights in different application scenarios, including social networking, crime detection, content ranking, and recommendations. While many graph processing systems working on top of distributed infrastructures have been proposed to deal with big graphs, the task of profiling their massive computations remains time consuming and error-prone. This paper presents GiViP, a visual profiler for distributed graph processing systems based on a Pregel-like computation model. GiViP captures the huge amount of messages exchanged throughout a computation and provides a powerful user interface for the visual analysis of the collected data. We discuss the effectiveness of our approach and show how to take advantage of GiViP to detect anomalies related to the computation and to the infrastructure, such as slow computing units, anomalous message patterns, unbalanced graph partitions, and links with high latency. (C) 2018 Elsevier B.V. All rights reserved.