Document Type

Conference Paper

Department/Unit

Department of Computer Science

Title

Improving large graph processing on partitioned graphs in the cloud

Language

English

Abstract

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 vertex-oriented graph processing. All optimizations are specifically designed for the cloud network environment. In experiments, we develop a system prototype following Pregel (the latest vertex-oriented graph engine by Google), and extend it with our graph partitioning framework. We conduct the experiments with a real-world social network and synthetic graphs over 100GB each in a local cluster and on Amazon EC2. Our experimental results demonstrate the efficiency of our graph partitioning framework, and the effectiveness of network performance aware optimizations on the large graph processing engine. © 2012 ACM.

Keywords

Cloud computing, Data center network, Graph partitioning, Large graph processing

Publication Date

2012

Source Publication Title

Proceedings of the Third ACM Symposium on Cloud Computing

Start Page

1

End Page

13

Conference Location

San Jose, United States

Publisher

ACM

DOI

10.1145/2391229.2391232

Link to Publisher's Edition

http://dx.doi.org/10.1145/2391229.2391232

ISBN (print)

9781450317610

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