Multi-Task Processing in Vertex-Centric Graph Systems: Evaluations and Insights

Vertex-centric (VC) graph systems are at the core of large-scale distributed graph processing. For such systems, a common usage pattern is the concurrent processing of multiple tasks (multiprocessing for short), which aims to execute a large number of unit tasks in parallel. In this paper, we point out that multi-processing has not been sufficiently studied or evaluated in previous work; hence, we fill this critical gap with three major contributions. First, we examine the tradeoff between two important measures in VC-systems: The number of communication rounds and message congestion. We show that this tradeoff is crucial to system performance; yet, existing approaches fail to achieve an optimal tradeoff, leading to poor performance. Second, based on extensive experimental evaluations on mainstream VC systems (e.g., Giraph, Pregel+, GraphD) and benchmark multi-processing tasks (e.g., Batch Personalized PageRanks, Multiple Source Shortest Paths), we present several important insights on the correlation between system performance and configurations, which is valuable to practitioners in optimizing system performance. Third, based on the insights drawn from our experimental evaluations, we present a cost-based tuning framework that optimizes the performance of a representative VC-system. This demonstrates the usefulness of the insights.