Large graph processing in the cloud
Rishan Chen, Xuetian Weng, Bingsheng He, Mao Yang · 2010
As the study of graphs, such as web and social graphs, becomes increasingly popular, the requirements of efficiency and programming flexibility of large graph processing tasks challenge existing tools. We propose to demonstrate Surfer, a large graph processing engine designed to execute in the cloud. Surfer provides two basic primitives for programmers - MapReduce and propagation. MapReduce, originally developed by Google, processes different key-value pairs in parallel, and propagation is an iterative computational pattern that transfers information along the edges from a vertex to its neighbors in the graph. These two primitives are complementary in graph processing. MapReduce is suitable for processing flat data structures, such as vertex-oriented tasks, and propagation is optimized for edge-oriented tasks on partitioned graphs.