Spinner: Scalable Graph Partitioning for the Cloud
Several organizations, like social networks, store and routinely analyze large graphs as part of their daily operation. Such graphs are typically distributed across multiple servers, and graph partitioning is critical for efficient graph management. Existing partitioning algorithms focus on finding graph partitions with good locality, but they disregard the pragmatic challenges of integrating partitioning into large-scale graph management systems deployed on a cloud. In this paper, the authors aim at a solution that performs substantially better than the most practical solution currently used, hash partitioning, but is nearly as practical.