On characterizing the performance of distributed graph computation platforms

Ahmed Barnawi, Omar Batarfi, Seyed Mehdi Reza Behteshi, Radwa Elshawi, Ayman Fayoumi, Reza Nouri, Sherif Sakr

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

1 Citation (Scopus)

Abstract

Graphs are widely used for modeling complicated data in different application domains such as social networks, protein networks, transportation networks, bibliographical networks, knowledge bases and many more. Currently, graphs with millions and billions of nodes and edges have become very common. Therefore, designing scalable systems for processing and analyzing large scale graphs has become one of the most timely problems facing the big data research community. In practice, distributed processing of large scale graphs is a challenging task due to their size in addition to their inherent irregular structure and the iterative nature of graph processing and computation algorithms. In recent years, several distributed graph processing systems have been presented, most notably Pregel and GraphLab, to tackle this challenge. In particular, both systems use a vertex-centric computation model which enables the user to design a program that is executed locally for each vertex in parallel. In this paper, we analyze the performance characteristics of distributed graph processing systems and provide an experimental comparison on the performance of two popular systems in this area.

Original languageEnglish
Title of host publicationPerformance Characterization and Benchmarking
Subtitle of host publicationTraditional to Big Data - 6th TPC Technology Conference, TPCTC 2014, Revised Selected Papers
PublisherSpringer Verlag
Pages29-43
Number of pages15
ISBN (Electronic)9783319153490
DOIs
Publication statusPublished - 1 Jan 2015
Event6th TPC Technology Conference on Performance Evaluation and Benchmarking, TPCTC 2014 held in conjunction with 40th International Conference on Very Large Data Bases, VLDB 2014 - Hangzhou, China
Duration: 1 Sep 20145 Sep 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8904
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other6th TPC Technology Conference on Performance Evaluation and Benchmarking, TPCTC 2014 held in conjunction with 40th International Conference on Very Large Data Bases, VLDB 2014
CountryChina
CityHangzhou
Period1/09/145/09/14

Fingerprint

Graph in graph theory
Processing
Vertex of a graph
Data structures
Distributed Processing
Transportation Networks
Data Modeling
Knowledge Base
Proteins
Social Networks
Irregular
Protein
Model
Big data

Cite this

Barnawi, A., Batarfi, O., Behteshi, S. M. R., Elshawi, R., Fayoumi, A., Nouri, R., & Sakr, S. (2015). On characterizing the performance of distributed graph computation platforms. In Performance Characterization and Benchmarking: Traditional to Big Data - 6th TPC Technology Conference, TPCTC 2014, Revised Selected Papers (pp. 29-43). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8904). Springer Verlag. https://doi.org/10.1007/978-3-319-15350-6_3
Barnawi, Ahmed ; Batarfi, Omar ; Behteshi, Seyed Mehdi Reza ; Elshawi, Radwa ; Fayoumi, Ayman ; Nouri, Reza ; Sakr, Sherif. / On characterizing the performance of distributed graph computation platforms. Performance Characterization and Benchmarking: Traditional to Big Data - 6th TPC Technology Conference, TPCTC 2014, Revised Selected Papers. Springer Verlag, 2015. pp. 29-43 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Barnawi, A, Batarfi, O, Behteshi, SMR, Elshawi, R, Fayoumi, A, Nouri, R & Sakr, S 2015, On characterizing the performance of distributed graph computation platforms. in Performance Characterization and Benchmarking: Traditional to Big Data - 6th TPC Technology Conference, TPCTC 2014, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8904, Springer Verlag, pp. 29-43, 6th TPC Technology Conference on Performance Evaluation and Benchmarking, TPCTC 2014 held in conjunction with 40th International Conference on Very Large Data Bases, VLDB 2014, Hangzhou, China, 1/09/14. https://doi.org/10.1007/978-3-319-15350-6_3

On characterizing the performance of distributed graph computation platforms. / Barnawi, Ahmed; Batarfi, Omar; Behteshi, Seyed Mehdi Reza; Elshawi, Radwa; Fayoumi, Ayman; Nouri, Reza; Sakr, Sherif.

Performance Characterization and Benchmarking: Traditional to Big Data - 6th TPC Technology Conference, TPCTC 2014, Revised Selected Papers. Springer Verlag, 2015. p. 29-43 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8904).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

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AB - Graphs are widely used for modeling complicated data in different application domains such as social networks, protein networks, transportation networks, bibliographical networks, knowledge bases and many more. Currently, graphs with millions and billions of nodes and edges have become very common. Therefore, designing scalable systems for processing and analyzing large scale graphs has become one of the most timely problems facing the big data research community. In practice, distributed processing of large scale graphs is a challenging task due to their size in addition to their inherent irregular structure and the iterative nature of graph processing and computation algorithms. In recent years, several distributed graph processing systems have been presented, most notably Pregel and GraphLab, to tackle this challenge. In particular, both systems use a vertex-centric computation model which enables the user to design a program that is executed locally for each vertex in parallel. In this paper, we analyze the performance characteristics of distributed graph processing systems and provide an experimental comparison on the performance of two popular systems in this area.

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Barnawi A, Batarfi O, Behteshi SMR, Elshawi R, Fayoumi A, Nouri R et al. On characterizing the performance of distributed graph computation platforms. In Performance Characterization and Benchmarking: Traditional to Big Data - 6th TPC Technology Conference, TPCTC 2014, Revised Selected Papers. Springer Verlag. 2015. p. 29-43. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-15350-6_3