PAGE: a partition aware graph computation engine
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Analysis of partitioning strategies for graph processing in bulk synchronous parallel models
Proceedings of the fifth international workshop on Cloud data management
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Graph partitioning is a key issue in graph database processing systems for achieving high efficiency on Cloud. However, the balanced graph partitioning itself is difficult because it is known to be NP-complete. In addition a static graph partitioning cannot keep all graph algorithms efficient for a long time in parallel on Cloud because the workload balancing in different iterations for different graph algorithms are all possible different. In this paper, we investigate graph behaviors by exploring the working window (we call it wind) changes, where a working window is a set of active vertices that a graph algorithm really needs to access in parallel computing. We investigated nine classic graph algorithms using real datasets, and propose simple yet effective policies that can achieve both high graph workload balancing and efficient partition on Cloud.