Memory-Optimized Distributed Graph Processing through Novel Compression Techniques

A multitude of contemporary applications now involve graph data whose size continuously grows and this trend shows no signs of subsiding. This has caused the emergence of many distributed graph processing systems including, among others, Pregel and Apache Giraph. By and large, these systems follow a vertex-centric model of computation weaved around processing supersteps enabling intuitive iterative programming and more importantly, allow for efficient parallel task execution. However, the unprecedented scale now reached by real-world graphs hardens the task of graph processing even in distributed environments and the current memory usage patterns rapidly become a primary concern for such contemporary graph processing systems. We seek to address this challenge by exploiting empirically-observed properties demonstrated by graphs that are generated by human activity. In this paper, we propose three space-efficient adjacency list representations that can be applied to any distributed graph processing system. Our suggested compact representations reduce respective memory requirements for accommodating the graph elements up to 5 times if compared with state-of-the-art methods. At the same time, our memory-optimized methods retain the efficiency of uncompressed structures. Last but not least, our suggested representations enable the execution of algorithms for large scale graphs in settings where contemporary alternative structures fail due to memory errors.