Abstract
Designing disk-based distributed graph systems has drawn a lot of research due to the strong expressiveness of the graph model and rapidly increasing graph volume. However, several challenges still exist in achieving both high computational efficiency and low network communication under the limitation of memory. In this paper, we design a novel distributed disk-based graph processing system, ScaleG, with a series of user-friendly programming interfaces. We propose several techniques to reduce both disk I/Os in each machine and message I/Os via the network. We manage all messages in memory and bound the volume of all messages by the number of vertices. We also carefully design the data structure to support partial computation and automatic vertex activation. We conduct extensive experiments on real-world big graphs to show the high efficiency of our system.
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