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Graph processing systems have been widely used in enterprises like online social networks to process their daily jobs. With the fast growing of social applications, they have to efficiently handle massive concurrent jobs. However, due to the inherent design for single job, existing systems incur great inefficiency in memory use and fault tolerance.(More)
In order to process very large graphs, existing graph processing systems, such as Pregel and Giraph, usually partition and distribute the graph computation on large number of nodes (i.e., workers). However, due to the heterogeneity of computing clusters (e.g., nodes with various bandwidth or CPU resource), blindly increasing the number of workers for a job(More)
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