Catch the Wind: Graph Workload Balancing on Cloud

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.