An experimental comparison of partitioning strategies in distributed graph processing

In this thesis, we study the problem of choosing among partitioning strategies in distributed graph processing systems. To this end, we evaluate and characterize both the performance and resource usage of different partitioning strategies under various popular distributed graph processing systems, applications, input graphs, and execution environments. Through our experiments, we found that no single partitioning strategy is the best fit for all situations, and that the choice of partitioning strategy has a significant effect on resource usage and application run-time. Our experiments demonstrate that the choice of partitioning strategy depends on (1) the degree distribution of input graph, (2) the type and duration of the application, and (3) the cluster size. Based on our results, we present rules of thumb to help users pick the best partitioning strategy for their particular use cases. We present results from each system, as well as from all partitioning strategies implemented in two common systems (PowerLyra and GraphX).

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2017

Author dc:creator

• Verma, Shiv

Contributors dc:contributor

• Gupta, Indranil

• Distributed
• Graph
• Processing
• Partitioning

Statement dc:rights

• Copyright 2017 Shiv Verma

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/97724

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/97724