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2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) (2016)
Cartagena, Colombia
May 16, 2016 to May 19, 2016
ISBN: 978-1-5090-2454-4
pp: 301-310
ABSTRACT
Large-scale graph analytics has gained attention during the past few years. As the world is going to be more connected by appearance of new technologies and applications such as social networks, Web portals, mobile devices, Internet of things, etc, a huge amount of data are created and stored every day in the form of graphs consisting of billions of vertices and edges. Many graph processing frameworks have been developed to process these large graphs since Google introduced its graph processing framework called Pregel in 2010. On the other hand, cloud computing which is a new paradigm of computing that overcomes restrictions of traditional problems in computing by enabling some novel technological and economical solutions such as distributed computing, elasticity and pay-as-you-go models has improved service delivery features. In this paper, we present iGiraph, a cost-efficient Pregel-like graph processing framework for processing large-scale graphs on public clouds. iGiraph uses a new dynamic re-partitioning approach based on messaging pattern to minimize the cost of resource utilization on public clouds. We also present the experimental results on the performance and cost effects of our method and compare them with basic Giraph framework. Our results validate that iGiraph remarkably decreases the cost and improves the performance by scaling the number of workers dynamically.
INDEX TERMS
Cloud computing, Partitioning algorithms, Classification algorithms, Computational modeling, Heuristic algorithms, Biological system modeling, Telecommunication traffic
CITATION

S. Heidari, R. N. Calheiros and R. Buyya, "iGiraph: A Cost-Efficient Framework for Processing Large-Scale Graphs on Public Clouds," 2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid)(CCGRID), Cartagena, Colombia, 2016, pp. 301-310.
doi:10.1109/CCGrid.2016.38
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