Distributed Edge Partitioning for Graph Processing

TL;DR

This paper introduces a novel edge partitioning framework for graph processing, demonstrating its efficiency and scalability through a distributed algorithm called d-fep, which improves parallelism in large graph analysis.

Contribution

It presents a new edge partitioning approach and a distributed algorithm, d-fep, for scalable graph processing, differing from traditional vertex partitioning methods.

Findings

d-fep is efficient and scalable

Edge partitioning improves parallel graph analysis

Framework is validated on cloud and simulations

Abstract

The availability of larger and larger graph datasets, growing exponentially over the years, has created several new algorithmic challenges to be addressed. Sequential approaches have become unfeasible, while interest on parallel and distributed algorithms has greatly increased. Appropriately partitioning the graph as a preprocessing step can improve the degree of parallelism of its analysis. A number of heuristic algorithms have been developed to solve this problem, but many of them subdivide the graph on its vertex set, thus obtaining a vertex-partitioned graph. Aim of this paper is to explore a completely different approach based on edge partitioning, in which edges, rather than vertices, are partitioned into disjoint subsets. Contribution of this paper is twofold: first, we introduce a graph processing framework based on edge partitioning, that is flexible enough to be applied to several different graph problems. Second, we show the feasibility of these ideas by presenting a distributed edge partitioning algorithm called d-fep. Our framework is thoroughly evaluated, using both simulations and an Hadoop implementation running on the Amazon EC2 cloud. The experiments show that d-fep is efficient, scalable and obtains consistently good partitions. The resulting edge-partitioned graph can be exploited to obtain more efficient implementations of graph analysis algorithms.