DFOGraph

Abstract

With the magnitude of graph-structured data continually increasing, graph processing systems that can scale-out and scale-up are needed to handle extreme-scale datasets. While existing distributed out-of-core solutions have made it possible, they suffer from limited performance due to excessive I/O and communication costs. We present DFOGraph, a distributed fully-out-of-core graph processing system that applies and assembles multiple techniques to enable I/O- and communication-efficient processing. DFOGraph builds upon two-level partitions with adaptive compressed representations to allow fine-grained selective computation and communication. Our evaluation shows DFOGraph outperforms Chaos and HybridGraph significantly (>12.94× and >10.82×) when scaling out to eight nodes.