Scalable and robust management of dynamic graph data

Most real-world networks evolve over time. This evolution can be modeled as a series of graphs that represent a network at different points in time. Our G∗ system enables efficient storage and querying of these graph snapshots by taking advantage of the commonalities among them. We are extending G∗ for highly scalable and robust operation. This paper shows that the classic challenges of data distribution and replication are imbued with renewed significance given continuously generated graph snapshots. Our data distribution technique adjusts the set of worker servers for storing each graph snapshot in a manner optimized for popular queries. Our data replication approach maintains each snapshot replica on a different number of workers, making available the most efficient replica configurations for different types of queries.