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Improving Large Graph Processing on Partitioned Graphs in the Cloud by Rishan Chen; Mao Yang; Xuetian Weng; Byron Choi; Bingsheng He; Xiaoming Li is a scholarly article available to read on EtoBox.

What is Improving Large Graph Processing on Partitioned Graphs in the Cloud about?

As the study of large graphs over hundreds of gigabytes becomes increasingly popular for various data-intensive applications in cloud computing, developing large graph processing systems has become a hot and fruitful research area. Many of those existing systems support a vertex-oriented execution model and allow users to develop custom logics on vertices. However, the inherently random access pattern on the vertex-oriented computation generates a significant amount of network traffic. While graph partitioning is known to be effective to reduce network traffic in graph processing, there is little attention given to how graph partitioning can be effectively integrated into large graph processing in the cloud environment. In this paper, we develop a novel graph partitioning framework to improve the network performance of graph partitioning itself, partitioned graph storage and vertexoriented graph processing. All optimizations are specifically designed for the cloud network environment. In experiments, we develop a system prototype following Pregel (the latest vertexoriented graph engine by Google), and extend it with our graph partitioning framework. We conduct the experiments with

Author

Rishan Chen; Mao Yang; Xuetian Weng; Byron Choi; Bingsheng He; Xiaoming Li

Publisher

ACM

Published

2012

Language

EN